Overview

Dataset statistics

Number of variables69
Number of observations33099
Missing cells186665
Missing cells (%)8.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory97.7 MiB
Average record size in memory3.0 KiB

Variable types

Categorical48
Numeric18
Unsupported3

Alerts

SG_UF has constant value "RJ"Constant
CD_MUNICIPIO_NASCIMENTO has constant value "-3"Constant
NM_UE has a high cardinality: 92 distinct valuesHigh cardinality
NM_CANDIDATO has a high cardinality: 30764 distinct valuesHigh cardinality
NM_URNA_CANDIDATO has a high cardinality: 29691 distinct valuesHigh cardinality
NM_EMAIL has a high cardinality: 12989 distinct valuesHigh cardinality
NM_COLIGACAO has a high cardinality: 350 distinct valuesHigh cardinality
DS_COMPOSICAO_COLIGACAO has a high cardinality: 364 distinct valuesHigh cardinality
NM_MUNICIPIO_NASCIMENTO has a high cardinality: 1231 distinct valuesHigh cardinality
DT_NASCIMENTO has a high cardinality: 13762 distinct valuesHigh cardinality
DS_OCUPACAO has a high cardinality: 226 distinct valuesHigh cardinality
CD_ELEICAO is highly overall correlated with ANO_ELEICAO and 17 other fieldsHigh correlation
CD_CARGO is highly overall correlated with ANO_ELEICAO and 15 other fieldsHigh correlation
SQ_CANDIDATO is highly overall correlated with ANO_ELEICAO and 18 other fieldsHigh correlation
NR_CANDIDATO is highly overall correlated with TP_ABRANGENCIA and 11 other fieldsHigh correlation
NR_CPF_CANDIDATO is highly overall correlated with NM_SOCIAL_CANDIDATO and 1 other fieldsHigh correlation
CD_DETALHE_SITUACAO_CAND is highly overall correlated with NM_SOCIAL_CANDIDATO and 10 other fieldsHigh correlation
NR_PARTIDO is highly overall correlated with NR_CANDIDATO and 7 other fieldsHigh correlation
SQ_COLIGACAO is highly overall correlated with ANO_ELEICAO and 14 other fieldsHigh correlation
NR_IDADE_DATA_POSSE is highly overall correlated with NM_SOCIAL_CANDIDATO and 2 other fieldsHigh correlation
NR_TITULO_ELEITORAL_CANDIDATO is highly overall correlated with NM_SOCIAL_CANDIDATO and 1 other fieldsHigh correlation
CD_GRAU_INSTRUCAO is highly overall correlated with NM_SOCIAL_CANDIDATO and 13 other fieldsHigh correlation
CD_ESTADO_CIVIL is highly overall correlated with NM_SOCIAL_CANDIDATO and 13 other fieldsHigh correlation
CD_COR_RACA is highly overall correlated with NM_SOCIAL_CANDIDATO and 13 other fieldsHigh correlation
CD_OCUPACAO is highly overall correlated with NM_SOCIAL_CANDIDATO and 13 other fieldsHigh correlation
CD_SIT_TOT_TURNO is highly overall correlated with NM_SOCIAL_CANDIDATO and 5 other fieldsHigh correlation
NR_PROCESSO is highly overall correlated with ANO_ELEICAO and 19 other fieldsHigh correlation
CD_SITUACAO_CANDIDATO_PLEITO is highly overall correlated with CD_SITUACAO_CANDIDATURA and 8 other fieldsHigh correlation
CD_SITUACAO_CANDIDATO_TOT is highly overall correlated with ANO_ELEICAO and 21 other fieldsHigh correlation
ANO_ELEICAO is highly overall correlated with CD_ELEICAO and 16 other fieldsHigh correlation
CD_TIPO_ELEICAO is highly overall correlated with NM_TIPO_ELEICAO and 5 other fieldsHigh correlation
NM_TIPO_ELEICAO is highly overall correlated with CD_TIPO_ELEICAO and 5 other fieldsHigh correlation
NR_TURNO is highly overall correlated with DT_ELEICAOHigh correlation
DS_ELEICAO is highly overall correlated with ANO_ELEICAO and 16 other fieldsHigh correlation
DT_ELEICAO is highly overall correlated with ANO_ELEICAO and 19 other fieldsHigh correlation
TP_ABRANGENCIA is highly overall correlated with ANO_ELEICAO and 14 other fieldsHigh correlation
NM_UE is highly overall correlated with ANO_ELEICAO and 17 other fieldsHigh correlation
DS_CARGO is highly overall correlated with ANO_ELEICAO and 15 other fieldsHigh correlation
NM_SOCIAL_CANDIDATO is highly overall correlated with ANO_ELEICAO and 42 other fieldsHigh correlation
CD_SITUACAO_CANDIDATURA is highly overall correlated with DT_ELEICAO and 10 other fieldsHigh correlation
DS_SITUACAO_CANDIDATURA is highly overall correlated with DT_ELEICAO and 10 other fieldsHigh correlation
DS_DETALHE_SITUACAO_CAND is highly overall correlated with NM_SOCIAL_CANDIDATO and 14 other fieldsHigh correlation
SG_PARTIDO is highly overall correlated with ANO_ELEICAO and 12 other fieldsHigh correlation
NM_PARTIDO is highly overall correlated with ANO_ELEICAO and 12 other fieldsHigh correlation
CD_NACIONALIDADE is highly overall correlated with NM_SOCIAL_CANDIDATO and 13 other fieldsHigh correlation
DS_NACIONALIDADE is highly overall correlated with NM_SOCIAL_CANDIDATO and 13 other fieldsHigh correlation
SG_UF_NASCIMENTO is highly overall correlated with NM_SOCIAL_CANDIDATO and 13 other fieldsHigh correlation
CD_GENERO is highly overall correlated with NM_SOCIAL_CANDIDATO and 14 other fieldsHigh correlation
DS_GENERO is highly overall correlated with NM_SOCIAL_CANDIDATO and 14 other fieldsHigh correlation
DS_GRAU_INSTRUCAO is highly overall correlated with NM_SOCIAL_CANDIDATO and 13 other fieldsHigh correlation
DS_ESTADO_CIVIL is highly overall correlated with NM_SOCIAL_CANDIDATO and 13 other fieldsHigh correlation
DS_COR_RACA is highly overall correlated with NM_SOCIAL_CANDIDATO and 13 other fieldsHigh correlation
DS_SIT_TOT_TURNO is highly overall correlated with NM_SOCIAL_CANDIDATO and 5 other fieldsHigh correlation
ST_REELEICAO is highly overall correlated with NM_SOCIAL_CANDIDATO and 14 other fieldsHigh correlation
ST_DECLARAR_BENS is highly overall correlated with NM_SOCIAL_CANDIDATO and 14 other fieldsHigh correlation
DS_SITUACAO_CANDIDATO_PLEITO is highly overall correlated with CD_SITUACAO_CANDIDATURA and 9 other fieldsHigh correlation
CD_SITUACAO_CANDIDATO_URNA is highly overall correlated with NM_UE and 10 other fieldsHigh correlation
DS_SITUACAO_CANDIDATO_URNA is highly overall correlated with NM_UE and 10 other fieldsHigh correlation
ST_CANDIDATO_INSERIDO_URNA is highly overall correlated with NM_SOCIAL_CANDIDATO and 3 other fieldsHigh correlation
NR_FEDERACAO is highly overall correlated with NR_CANDIDATO and 9 other fieldsHigh correlation
NM_FEDERACAO is highly overall correlated with NR_CANDIDATO and 9 other fieldsHigh correlation
SG_FEDERACAO is highly overall correlated with NR_CANDIDATO and 9 other fieldsHigh correlation
DS_COMPOSICAO_FEDERACAO is highly overall correlated with NR_CANDIDATO and 9 other fieldsHigh correlation
NM_TIPO_DESTINACAO_VOTOS is highly overall correlated with DS_SITUACAO_CANDIDATO_PLEITO and 2 other fieldsHigh correlation
DS_SITUACAO_CANDIDATO_TOT is highly overall correlated with ANO_ELEICAO and 21 other fieldsHigh correlation
ST_PREST_CONTAS is highly overall correlated with ANO_ELEICAO and 14 other fieldsHigh correlation
TP_AGREMIACAO is highly overall correlated with ANO_ELEICAO and 8 other fieldsHigh correlation
NM_SOCIAL_CANDIDATO has 33023 (99.8%) missing valuesMissing
CD_SIT_TOT_TURNO has 885 (2.7%) missing valuesMissing
DS_SIT_TOT_TURNO has 885 (2.7%) missing valuesMissing
NR_PROTOCOLO_CANDIDATURA has 33099 (100.0%) missing valuesMissing
CD_SITUACAO_CANDIDATO_PLEITO has 885 (2.7%) missing valuesMissing
DS_SITUACAO_CANDIDATO_PLEITO has 885 (2.7%) missing valuesMissing
DS_SITUACAO_CANDIDATO_URNA has 885 (2.7%) missing valuesMissing
NM_FEDERACAO has 32764 (99.0%) missing valuesMissing
SG_FEDERACAO has 32764 (99.0%) missing valuesMissing
DS_COMPOSICAO_FEDERACAO has 32764 (99.0%) missing valuesMissing
NM_TIPO_DESTINACAO_VOTOS has 5072 (15.3%) missing valuesMissing
CD_SITUACAO_CANDIDATO_TOT has 4437 (13.4%) missing valuesMissing
DS_SITUACAO_CANDIDATO_TOT has 4437 (13.4%) missing valuesMissing
ST_PREST_CONTAS has 3699 (11.2%) missing valuesMissing
NM_CANDIDATO is uniformly distributedUniform
NM_URNA_CANDIDATO is uniformly distributedUniform
SG_UE is an unsupported type, check if it needs cleaning or further analysisUnsupported
VR_DESPESA_MAX_CAMPANHA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NR_PROTOCOLO_CANDIDATURA is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2022-12-06 15:46:53.019375
Analysis finished2022-12-06 15:48:44.037548
Duration1 minute and 51.02 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

ANO_ELEICAO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2020
26615 
2018
3699 
2022
2785 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters132396
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2020 26615
80.4%
2018 3699
 
11.2%
2022 2785
 
8.4%

Length

2022-12-06T12:48:44.144969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:44.273777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 26615
80.4%
2018 3699
 
11.2%
2022 2785
 
8.4%

Most occurring characters

ValueCountFrequency (%)
2 65284
49.3%
0 59714
45.1%
1 3699
 
2.8%
8 3699
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132396
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 65284
49.3%
0 59714
45.1%
1 3699
 
2.8%
8 3699
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 132396
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 65284
49.3%
0 59714
45.1%
1 3699
 
2.8%
8 3699
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 65284
49.3%
0 59714
45.1%
1 3699
 
2.8%
8 3699
 
2.8%

CD_TIPO_ELEICAO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2
33042 
1
 
57

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33099
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 33042
99.8%
1 57
 
0.2%

Length

2022-12-06T12:48:44.349434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:44.441897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 33042
99.8%
1 57
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 33042
99.8%
1 57
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33099
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 33042
99.8%
1 57
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 33099
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 33042
99.8%
1 57
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 33042
99.8%
1 57
 
0.2%

NM_TIPO_ELEICAO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
ELEIÇÃO ORDINÁRIA
33042 
ELEIÇÃO SUPLEMENTAR
 
57

Length

Max length19
Median length17
Mean length17.003444
Min length17

Characters and Unicode

Total characters562797
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowELEIÇÃO ORDINÁRIA
2nd rowELEIÇÃO ORDINÁRIA
3rd rowELEIÇÃO ORDINÁRIA
4th rowELEIÇÃO ORDINÁRIA
5th rowELEIÇÃO ORDINÁRIA

Common Values

ValueCountFrequency (%)
ELEIÇÃO ORDINÁRIA 33042
99.8%
ELEIÇÃO SUPLEMENTAR 57
 
0.2%

Length

2022-12-06T12:48:44.523781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:44.646341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
eleição 33099
50.0%
ordinária 33042
49.9%
suplementar 57
 
0.1%

Most occurring characters

ValueCountFrequency (%)
I 99183
17.6%
E 66312
11.8%
O 66141
11.8%
R 66141
11.8%
L 33156
 
5.9%
A 33099
 
5.9%
Ç 33099
 
5.9%
à 33099
 
5.9%
33099
 
5.9%
N 33099
 
5.9%
Other values (7) 66369
11.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 529698
94.1%
Space Separator 33099
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 99183
18.7%
E 66312
12.5%
O 66141
12.5%
R 66141
12.5%
L 33156
 
6.3%
A 33099
 
6.2%
Ç 33099
 
6.2%
à 33099
 
6.2%
N 33099
 
6.2%
D 33042
 
6.2%
Other values (6) 33327
 
6.3%
Space Separator
ValueCountFrequency (%)
33099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 529698
94.1%
Common 33099
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 99183
18.7%
E 66312
12.5%
O 66141
12.5%
R 66141
12.5%
L 33156
 
6.3%
A 33099
 
6.2%
Ç 33099
 
6.2%
à 33099
 
6.2%
N 33099
 
6.2%
D 33042
 
6.2%
Other values (6) 33327
 
6.3%
Common
ValueCountFrequency (%)
33099
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 463557
82.4%
None 99240
 
17.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 99183
21.4%
E 66312
14.3%
O 66141
14.3%
R 66141
14.3%
L 33156
 
7.2%
A 33099
 
7.1%
33099
 
7.1%
N 33099
 
7.1%
D 33042
 
7.1%
S 57
 
< 0.1%
Other values (4) 228
 
< 0.1%
None
ValueCountFrequency (%)
Ç 33099
33.4%
à 33099
33.4%
Á 33042
33.3%

NR_TURNO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
1
33075 
2
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33099
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 33075
99.9%
2 24
 
0.1%

Length

2022-12-06T12:48:44.720157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:44.822125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 33075
99.9%
2 24
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 33075
99.9%
2 24
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33099
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 33075
99.9%
2 24
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 33099
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 33075
99.9%
2 24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 33075
99.9%
2 24
 
0.1%

CD_ELEICAO
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean421.82141
Minimum297
Maximum551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.0 KiB
2022-12-06T12:48:44.891644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum297
5-th percentile297
Q1426
median426
Q3426
95-th percentile546
Maximum551
Range254
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.382387
Coefficient of variation (CV)0.13129345
Kurtosis2.0292508
Mean421.82141
Median Absolute Deviation (MAD)0
Skewness-0.32157418
Sum13961867
Variance3067.2088
MonotonicityNot monotonic
2022-12-06T12:48:44.975750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
426 26538
80.2%
297 3695
 
11.2%
546 2785
 
8.4%
427 20
 
0.1%
507 12
 
< 0.1%
551 12
 
< 0.1%
458 8
 
< 0.1%
532 7
 
< 0.1%
506 6
 
< 0.1%
508 6
 
< 0.1%
Other values (2) 10
 
< 0.1%
ValueCountFrequency (%)
297 3695
 
11.2%
298 4
 
< 0.1%
426 26538
80.2%
427 20
 
0.1%
458 8
 
< 0.1%
459 6
 
< 0.1%
506 6
 
< 0.1%
507 12
 
< 0.1%
508 6
 
< 0.1%
532 7
 
< 0.1%
ValueCountFrequency (%)
551 12
 
< 0.1%
546 2785
 
8.4%
532 7
 
< 0.1%
508 6
 
< 0.1%
507 12
 
< 0.1%
506 6
 
< 0.1%
459 6
 
< 0.1%
458 8
 
< 0.1%
427 20
 
0.1%
426 26538
80.2%

DS_ELEICAO
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Eleições Municipais 2020
26558 
Eleições Gerais Estaduais 2018
3699 
Eleições Gerais Estaduais 2022
2785 
RJ - Suplementar de Itatiaia
 
24
RJ - Suplementar de Sta. Ma. Madalena
 
12
Other values (3)
 
21

Length

Max length37
Median length24
Mean length25.186713
Min length24

Characters and Unicode

Total characters833655
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEleições Gerais Estaduais 2018
2nd rowEleições Gerais Estaduais 2018
3rd rowEleições Gerais Estaduais 2018
4th rowEleições Gerais Estaduais 2018
5th rowEleições Gerais Estaduais 2018

Common Values

ValueCountFrequency (%)
Eleições Municipais 2020 26558
80.2%
Eleições Gerais Estaduais 2018 3699
 
11.2%
Eleições Gerais Estaduais 2022 2785
 
8.4%
RJ - Suplementar de Itatiaia 24
 
0.1%
RJ - Suplementar de Sta. Ma. Madalena 12
 
< 0.1%
RJ - Suplementar de Itatiaia 8
 
< 0.1%
RJ - Suplementar de Carapebus 7
 
< 0.1%
RJ - Suplementar de Silva Jardim 6
 
< 0.1%

Length

2022-12-06T12:48:45.067556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:45.188764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
eleições 33042
31.2%
municipais 26558
25.1%
2020 26558
25.1%
gerais 6484
 
6.1%
estaduais 6484
 
6.1%
2018 3699
 
3.5%
2022 2785
 
2.6%
de 57
 
0.1%
suplementar 57
 
0.1%
57
 
0.1%
Other values (8) 144
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 125760
15.1%
s 79059
 
9.5%
72834
 
8.7%
e 72758
 
8.7%
2 65170
 
7.8%
0 59600
 
7.1%
a 46249
 
5.5%
E 39526
 
4.7%
l 33117
 
4.0%
u 33106
 
4.0%
Other values (22) 206476
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 555746
66.7%
Decimal Number 132168
 
15.9%
Space Separator 72834
 
8.7%
Uppercase Letter 72826
 
8.7%
Dash Punctuation 57
 
< 0.1%
Other Punctuation 24
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 125760
22.6%
s 79059
14.2%
e 72758
13.1%
a 46249
 
8.3%
l 33117
 
6.0%
u 33106
 
6.0%
õ 33042
 
5.9%
ç 33042
 
5.9%
n 26627
 
4.8%
p 26622
 
4.8%
Other values (7) 46364
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
E 39526
54.3%
M 26582
36.5%
G 6484
 
8.9%
S 75
 
0.1%
J 63
 
0.1%
R 57
 
0.1%
I 32
 
< 0.1%
C 7
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 65170
49.3%
0 59600
45.1%
1 3699
 
2.8%
8 3699
 
2.8%
Space Separator
ValueCountFrequency (%)
72834
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%
Other Punctuation
ValueCountFrequency (%)
. 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 628572
75.4%
Common 205083
 
24.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 125760
20.0%
s 79059
12.6%
e 72758
11.6%
a 46249
 
7.4%
E 39526
 
6.3%
l 33117
 
5.3%
u 33106
 
5.3%
õ 33042
 
5.3%
ç 33042
 
5.3%
n 26627
 
4.2%
Other values (15) 106286
16.9%
Common
ValueCountFrequency (%)
72834
35.5%
2 65170
31.8%
0 59600
29.1%
1 3699
 
1.8%
8 3699
 
1.8%
- 57
 
< 0.1%
. 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 767571
92.1%
None 66084
 
7.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 125760
16.4%
s 79059
10.3%
72834
9.5%
e 72758
9.5%
2 65170
8.5%
0 59600
7.8%
a 46249
 
6.0%
E 39526
 
5.1%
l 33117
 
4.3%
u 33106
 
4.3%
Other values (20) 140392
18.3%
None
ValueCountFrequency (%)
õ 33042
50.0%
ç 33042
50.0%

DT_ELEICAO
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
15/11/2020
26538 
07/10/2018
3695 
02/10/2022
2785 
12/09/2021
 
24
29/11/2020
 
20
Other values (4)
 
37

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters330990
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row07/10/2018
2nd row07/10/2018
3rd row07/10/2018
4th row07/10/2018
5th row07/10/2018

Common Values

ValueCountFrequency (%)
15/11/2020 26538
80.2%
07/10/2018 3695
 
11.2%
02/10/2022 2785
 
8.4%
12/09/2021 24
 
0.1%
29/11/2020 20
 
0.1%
11/04/2021 14
 
< 0.1%
13/03/2022 12
 
< 0.1%
07/11/2021 7
 
< 0.1%
28/10/2018 4
 
< 0.1%

Length

2022-12-06T12:48:45.302193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:45.421412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
15/11/2020 26538
80.2%
07/10/2018 3695
 
11.2%
02/10/2022 2785
 
8.4%
12/09/2021 24
 
0.1%
29/11/2020 20
 
0.1%
11/04/2021 14
 
< 0.1%
13/03/2022 12
 
< 0.1%
07/11/2021 7
 
< 0.1%
28/10/2018 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 89960
27.2%
0 72678
22.0%
2 68129
20.6%
/ 66198
20.0%
5 26538
 
8.0%
8 3703
 
1.1%
7 3702
 
1.1%
9 44
 
< 0.1%
3 24
 
< 0.1%
4 14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 264792
80.0%
Other Punctuation 66198
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 89960
34.0%
0 72678
27.4%
2 68129
25.7%
5 26538
 
10.0%
8 3703
 
1.4%
7 3702
 
1.4%
9 44
 
< 0.1%
3 24
 
< 0.1%
4 14
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 66198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 330990
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 89960
27.2%
0 72678
22.0%
2 68129
20.6%
/ 66198
20.0%
5 26538
 
8.0%
8 3703
 
1.1%
7 3702
 
1.1%
9 44
 
< 0.1%
3 24
 
< 0.1%
4 14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 330990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 89960
27.2%
0 72678
22.0%
2 68129
20.6%
/ 66198
20.0%
5 26538
 
8.0%
8 3703
 
1.1%
7 3702
 
1.1%
9 44
 
< 0.1%
3 24
 
< 0.1%
4 14
 
< 0.1%

TP_ABRANGENCIA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
MUNICIPAL
26615 
ESTADUAL
6484 

Length

Max length9
Median length9
Mean length8.8041028
Min length8

Characters and Unicode

Total characters291407
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTADUAL
2nd rowESTADUAL
3rd rowESTADUAL
4th rowESTADUAL
5th rowESTADUAL

Common Values

ValueCountFrequency (%)
MUNICIPAL 26615
80.4%
ESTADUAL 6484
 
19.6%

Length

2022-12-06T12:48:45.525503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:45.618421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
municipal 26615
80.4%
estadual 6484
 
19.6%

Most occurring characters

ValueCountFrequency (%)
I 53230
18.3%
A 39583
13.6%
U 33099
11.4%
L 33099
11.4%
M 26615
9.1%
N 26615
9.1%
C 26615
9.1%
P 26615
9.1%
E 6484
 
2.2%
S 6484
 
2.2%
Other values (2) 12968
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 291407
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 53230
18.3%
A 39583
13.6%
U 33099
11.4%
L 33099
11.4%
M 26615
9.1%
N 26615
9.1%
C 26615
9.1%
P 26615
9.1%
E 6484
 
2.2%
S 6484
 
2.2%
Other values (2) 12968
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 291407
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 53230
18.3%
A 39583
13.6%
U 33099
11.4%
L 33099
11.4%
M 26615
9.1%
N 26615
9.1%
C 26615
9.1%
P 26615
9.1%
E 6484
 
2.2%
S 6484
 
2.2%
Other values (2) 12968
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 291407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 53230
18.3%
A 39583
13.6%
U 33099
11.4%
L 33099
11.4%
M 26615
9.1%
N 26615
9.1%
C 26615
9.1%
P 26615
9.1%
E 6484
 
2.2%
S 6484
 
2.2%
Other values (2) 12968
 
4.5%

SG_UF
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
RJ
33099 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters66198
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRJ
2nd rowRJ
3rd rowRJ
4th rowRJ
5th rowRJ

Common Values

ValueCountFrequency (%)
RJ 33099
100.0%

Length

2022-12-06T12:48:45.690526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:45.781003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
rj 33099
100.0%

Most occurring characters

ValueCountFrequency (%)
R 33099
50.0%
J 33099
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 66198
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 33099
50.0%
J 33099
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66198
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 33099
50.0%
J 33099
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 33099
50.0%
J 33099
50.0%

SG_UE
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.3 MiB

NM_UE
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct92
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
RIO DE JANEIRO
8332 
SÃO GONÇALO
 
1096
CAMPOS DOS GOYTACAZES
 
847
DUQUE DE CAXIAS
 
742
SÃO JOÃO DE MERITI
 
734
Other values (87)
21348 

Length

Max length29
Median length23
Mean length12.212907
Min length4

Characters and Unicode

Total characters404235
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRIO DE JANEIRO
2nd rowRIO DE JANEIRO
3rd rowRIO DE JANEIRO
4th rowRIO DE JANEIRO
5th rowRIO DE JANEIRO

Common Values

ValueCountFrequency (%)
RIO DE JANEIRO 8332
25.2%
SÃO GONÇALO 1096
 
3.3%
CAMPOS DOS GOYTACAZES 847
 
2.6%
DUQUE DE CAXIAS 742
 
2.2%
SÃO JOÃO DE MERITI 734
 
2.2%
NITERÓI 733
 
2.2%
VOLTA REDONDA 610
 
1.8%
NOVA FRIBURGO 608
 
1.8%
MAGÉ 598
 
1.8%
BELFORD ROXO 590
 
1.8%
Other values (82) 18209
55.0%

Length

2022-12-06T12:48:45.857104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 11272
 
15.5%
rio 9291
 
12.8%
janeiro 8332
 
11.4%
são 2954
 
4.1%
do 1503
 
2.1%
dos 1487
 
2.0%
gonçalo 1096
 
1.5%
nova 1095
 
1.5%
joão 874
 
1.2%
barra 849
 
1.2%
Other values (124) 34046
46.8%

Most occurring characters

ValueCountFrequency (%)
A 49299
12.2%
O 45363
11.2%
39700
9.8%
R 38690
9.6%
I 37168
9.2%
E 35147
 
8.7%
D 21866
 
5.4%
S 18282
 
4.5%
N 18201
 
4.5%
J 10436
 
2.6%
Other values (25) 90083
22.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 364468
90.2%
Space Separator 39700
 
9.8%
Dash Punctuation 67
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 49299
13.5%
O 45363
12.4%
R 38690
10.6%
I 37168
10.2%
E 35147
9.6%
D 21866
 
6.0%
S 18282
 
5.0%
N 18201
 
5.0%
J 10436
 
2.9%
T 10125
 
2.8%
Other values (23) 79891
21.9%
Space Separator
ValueCountFrequency (%)
39700
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 364468
90.2%
Common 39767
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 49299
13.5%
O 45363
12.4%
R 38690
10.6%
I 37168
10.2%
E 35147
9.6%
D 21866
 
6.0%
S 18282
 
5.0%
N 18201
 
5.0%
J 10436
 
2.9%
T 10125
 
2.8%
Other values (23) 79891
21.9%
Common
ValueCountFrequency (%)
39700
99.8%
- 67
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 390289
96.6%
None 13946
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 49299
12.6%
O 45363
11.6%
39700
10.2%
R 38690
9.9%
I 37168
9.5%
E 35147
9.0%
D 21866
 
5.6%
S 18282
 
4.7%
N 18201
 
4.7%
J 10436
 
2.7%
Other values (16) 76137
19.5%
None
ValueCountFrequency (%)
à 4456
32.0%
Ç 2233
16.0%
Ó 1966
14.1%
É 1950
14.0%
Í 1417
 
10.2%
Á 889
 
6.4%
Ê 385
 
2.8%
Ú 383
 
2.7%
Ô 267
 
1.9%

CD_CARGO
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.696214
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.0 KiB
2022-12-06T12:48:45.949405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q113
median13
Q313
95-th percentile13
Maximum13
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.527404
Coefficient of variation (CV)0.21608735
Kurtosis0.48647866
Mean11.696214
Median Absolute Deviation (MAD)0
Skewness-1.5331963
Sum387133
Variance6.3877707
MonotonicityNot monotonic
2022-12-06T12:48:47.293772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
13 25311
76.5%
7 4104
 
12.4%
6 2237
 
6.8%
12 663
 
2.0%
11 641
 
1.9%
10 33
 
0.1%
5 31
 
0.1%
9 31
 
0.1%
4 25
 
0.1%
3 23
 
0.1%
ValueCountFrequency (%)
3 23
 
0.1%
4 25
 
0.1%
5 31
 
0.1%
6 2237
 
6.8%
7 4104
 
12.4%
9 31
 
0.1%
10 33
 
0.1%
11 641
 
1.9%
12 663
 
2.0%
13 25311
76.5%
ValueCountFrequency (%)
13 25311
76.5%
12 663
 
2.0%
11 641
 
1.9%
10 33
 
0.1%
9 31
 
0.1%
7 4104
 
12.4%
6 2237
 
6.8%
5 31
 
0.1%
4 25
 
0.1%
3 23
 
0.1%

DS_CARGO
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
VEREADOR
25311 
DEPUTADO ESTADUAL
4104 
DEPUTADO FEDERAL
 
2237
VICE-PREFEITO
 
663
PREFEITO
 
641
Other values (5)
 
143

Length

Max length17
Median length8
Mean length9.7683012
Min length7

Characters and Unicode

Total characters323321
Distinct characters21
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEPUTADO ESTADUAL
2nd rowDEPUTADO ESTADUAL
3rd rowDEPUTADO FEDERAL
4th rowDEPUTADO ESTADUAL
5th rowDEPUTADO FEDERAL

Common Values

ValueCountFrequency (%)
VEREADOR 25311
76.5%
DEPUTADO ESTADUAL 4104
 
12.4%
DEPUTADO FEDERAL 2237
 
6.8%
VICE-PREFEITO 663
 
2.0%
PREFEITO 641
 
1.9%
2º SUPLENTE 33
 
0.1%
SENADOR 31
 
0.1%
1º SUPLENTE 31
 
0.1%
VICE-GOVERNADOR 25
 
0.1%
GOVERNADOR 23
 
0.1%

Length

2022-12-06T12:48:47.385250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:47.519319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
vereador 25311
64.1%
deputado 6341
 
16.1%
estadual 4104
 
10.4%
federal 2237
 
5.7%
vice-prefeito 663
 
1.7%
prefeito 641
 
1.6%
suplente 64
 
0.2%
33
 
0.1%
senador 31
 
0.1%
31
 
0.1%
Other values (2) 48
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 69044
21.4%
R 54290
16.8%
D 44413
13.7%
A 42176
13.0%
O 33083
10.2%
V 26047
 
8.1%
T 11813
 
3.7%
U 10509
 
3.3%
P 7709
 
2.4%
L 6405
 
2.0%
Other values (11) 17832
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 316100
97.8%
Space Separator 6405
 
2.0%
Dash Punctuation 688
 
0.2%
Other Letter 64
 
< 0.1%
Decimal Number 64
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 69044
21.8%
R 54290
17.2%
D 44413
14.1%
A 42176
13.3%
O 33083
10.5%
V 26047
 
8.2%
T 11813
 
3.7%
U 10509
 
3.3%
P 7709
 
2.4%
L 6405
 
2.0%
Other values (6) 10611
 
3.4%
Decimal Number
ValueCountFrequency (%)
2 33
51.6%
1 31
48.4%
Space Separator
ValueCountFrequency (%)
6405
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 688
100.0%
Other Letter
ValueCountFrequency (%)
º 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 316164
97.8%
Common 7157
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 69044
21.8%
R 54290
17.2%
D 44413
14.0%
A 42176
13.3%
O 33083
10.5%
V 26047
 
8.2%
T 11813
 
3.7%
U 10509
 
3.3%
P 7709
 
2.4%
L 6405
 
2.0%
Other values (7) 10675
 
3.4%
Common
ValueCountFrequency (%)
6405
89.5%
- 688
 
9.6%
2 33
 
0.5%
1 31
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 323257
> 99.9%
None 64
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 69044
21.4%
R 54290
16.8%
D 44413
13.7%
A 42176
13.0%
O 33083
10.2%
V 26047
 
8.1%
T 11813
 
3.7%
U 10509
 
3.3%
P 7709
 
2.4%
L 6405
 
2.0%
Other values (10) 17768
 
5.5%
None
ValueCountFrequency (%)
º 64
100.0%

SQ_CANDIDATO
Real number (ℝ)

Distinct33075
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9000097 × 1011
Minimum1.900006 × 1011
Maximum1.9000174 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.0 KiB
2022-12-06T12:48:47.635247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.900006 × 1011
5-th percentile1.9000061 × 1011
Q11.9000074 × 1011
median1.9000094 × 1011
Q31.9000114 × 1011
95-th percentile1.9000161 × 1011
Maximum1.9000174 × 1011
Range1138173
Interquartile range (IQR)393397

Descriptive statistics

Standard deviation286298.12
Coefficient of variation (CV)1.5068245 × 10-6
Kurtosis0.11315056
Mean1.9000097 × 1011
Median Absolute Deviation (MAD)197381
Skewness0.80256267
Sum6.2888421 × 1015
Variance8.1966612 × 1010
MonotonicityNot monotonic
2022-12-06T12:48:47.744972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190000640057 2
 
< 0.1%
190000775862 2
 
< 0.1%
190000857140 2
 
< 0.1%
190001104234 2
 
< 0.1%
190001104235 2
 
< 0.1%
190000609935 2
 
< 0.1%
190001009041 2
 
< 0.1%
190000640058 2
 
< 0.1%
190000684239 2
 
< 0.1%
190000609934 2
 
< 0.1%
Other values (33065) 33079
99.9%
ValueCountFrequency (%)
190000601071 1
< 0.1%
190000601072 1
< 0.1%
190000601073 1
< 0.1%
190000601074 1
< 0.1%
190000601075 1
< 0.1%
190000601076 1
< 0.1%
190000601077 1
< 0.1%
190000601078 1
< 0.1%
190000601079 1
< 0.1%
190000601080 1
< 0.1%
ValueCountFrequency (%)
190001739244 1
< 0.1%
190001739242 1
< 0.1%
190001738893 1
< 0.1%
190001738889 1
< 0.1%
190001738883 1
< 0.1%
190001738882 1
< 0.1%
190001738869 1
< 0.1%
190001738867 1
< 0.1%
190001738844 1
< 0.1%
190001738795 1
< 0.1%

NR_CANDIDATO
Real number (ℝ)

Distinct10288
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30802.52
Minimum10
Maximum90999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.0 KiB
2022-12-06T12:48:47.867278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile1224.9
Q113334.5
median23223
Q345100
95-th percentile77456
Maximum90999
Range90989
Interquartile range (IQR)31765.5

Descriptive statistics

Standard deviation23286.754
Coefficient of variation (CV)0.75600158
Kurtosis-0.0075901807
Mean30802.52
Median Absolute Deviation (MAD)12212
Skewness0.9166122
Sum1.0195326 × 109
Variance5.422729 × 108
MonotonicityNot monotonic
2022-12-06T12:48:47.983100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 84
 
0.3%
10 81
 
0.2%
77 81
 
0.2%
10123 77
 
0.2%
77123 76
 
0.2%
12 73
 
0.2%
55 73
 
0.2%
77777 71
 
0.2%
10000 69
 
0.2%
25 68
 
0.2%
Other values (10278) 32346
97.7%
ValueCountFrequency (%)
10 81
0.2%
11 50
0.2%
12 73
0.2%
13 56
0.2%
14 35
0.1%
15 57
0.2%
16 19
 
0.1%
17 48
0.1%
18 12
 
< 0.1%
19 34
0.1%
ValueCountFrequency (%)
90999 39
0.1%
90998 1
 
< 0.1%
90997 1
 
< 0.1%
90991 1
 
< 0.1%
90990 3
 
< 0.1%
90987 2
 
< 0.1%
90979 2
 
< 0.1%
90977 2
 
< 0.1%
90970 1
 
< 0.1%
90964 1
 
< 0.1%

NM_CANDIDATO
Categorical

HIGH CARDINALITY
UNIFORM

Distinct30764
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
CARLOS ALBERTO DA SILVA
 
11
LUIS CARLOS DA SILVA
 
7
DENILSON SAMPAIO DA SILVA
 
5
JOSE CARLOS DA SILVA
 
5
IRINEU NOGUEIRA COELHO
 
4
Other values (30759)
33067 

Length

Max length59
Median length50
Mean length25.385722
Min length4

Characters and Unicode

Total characters840242
Distinct characters45
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28753 ?
Unique (%)86.9%

Sample

1st rowNORBERTO JOSE FERREIRA
2nd rowMARISA FRANCISCA DA SILVA LIVRAMENTO
3rd rowGILSON CARVALHO VILELA
4th rowANA CLAUDIA COTTAS SILVA
5th rowANDREIA ALMEIDA ZITO DOS SANTOS

Common Values

ValueCountFrequency (%)
CARLOS ALBERTO DA SILVA 11
 
< 0.1%
LUIS CARLOS DA SILVA 7
 
< 0.1%
DENILSON SAMPAIO DA SILVA 5
 
< 0.1%
JOSE CARLOS DA SILVA 5
 
< 0.1%
IRINEU NOGUEIRA COELHO 4
 
< 0.1%
MARCO ANTONIO DE OLIVEIRA 4
 
< 0.1%
LEONARDO VIEIRA MENDES 4
 
< 0.1%
CAIO SANTOS VIANNA 4
 
< 0.1%
JOSÉ CARLOS DA SILVA 4
 
< 0.1%
BRUNO GUIMARÃES DINIZ 4
 
< 0.1%
Other values (30754) 33047
99.8%

Length

2022-12-06T12:48:48.121641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 9304
 
7.1%
da 7164
 
5.5%
silva 6524
 
5.0%
santos 3067
 
2.3%
oliveira 2501
 
1.9%
dos 2453
 
1.9%
souza 2348
 
1.8%
carlos 1494
 
1.1%
pereira 1332
 
1.0%
maria 1260
 
1.0%
Other values (11008) 93555
71.4%

Most occurring characters

ValueCountFrequency (%)
A 110279
13.1%
98208
11.7%
E 74254
8.8%
O 69713
 
8.3%
I 66697
 
7.9%
R 64220
 
7.6%
S 56243
 
6.7%
L 43777
 
5.2%
N 40805
 
4.9%
D 39813
 
4.7%
Other values (35) 176233
21.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 742028
88.3%
Space Separator 98208
 
11.7%
Dash Punctuation 4
 
< 0.1%
Modifier Symbol 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 110279
14.9%
E 74254
10.0%
O 69713
9.4%
I 66697
9.0%
R 64220
8.7%
S 56243
 
7.6%
L 43777
 
5.9%
N 40805
 
5.5%
D 39813
 
5.4%
C 23686
 
3.2%
Other values (31) 152541
20.6%
Space Separator
ValueCountFrequency (%)
98208
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 742028
88.3%
Common 98214
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 110279
14.9%
E 74254
10.0%
O 69713
9.4%
I 66697
9.0%
R 64220
8.7%
S 56243
 
7.6%
L 43777
 
5.9%
N 40805
 
5.5%
D 39813
 
5.4%
C 23686
 
3.2%
Other values (31) 152541
20.6%
Common
ValueCountFrequency (%)
98208
> 99.9%
- 4
 
< 0.1%
´ 1
 
< 0.1%
. 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 833172
99.2%
None 7070
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 110279
13.2%
98208
11.8%
E 74254
8.9%
O 69713
8.4%
I 66697
 
8.0%
R 64220
 
7.7%
S 56243
 
6.8%
L 43777
 
5.3%
N 40805
 
4.9%
D 39813
 
4.8%
Other values (19) 169163
20.3%
None
ValueCountFrequency (%)
É 1753
24.8%
à 1633
23.1%
Ç 1583
22.4%
Á 711
10.1%
Í 314
 
4.4%
Ú 295
 
4.2%
Ô 268
 
3.8%
Ê 196
 
2.8%
Ó 149
 
2.1%
 129
 
1.8%
Other values (6) 39
 
0.6%

NM_URNA_CANDIDATO
Categorical

HIGH CARDINALITY
UNIFORM

Distinct29691
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
ANA PAULA
 
18
FABINHO
 
12
ANA CLAUDIA
 
10
PEDRO PAULO
 
9
JUNINHO
 
8
Other values (29686)
33042 

Length

Max length30
Median length26
Mean length14.174054
Min length2

Characters and Unicode

Total characters469147
Distinct characters64
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27134 ?
Unique (%)82.0%

Sample

1st rowNORBERTTO FERRERA
2nd rowMARISA LIVRAMENTO
3rd rowVILELA
4th rowCLAUDIA COTTAS
5th rowANDREIA ZITO

Common Values

ValueCountFrequency (%)
ANA PAULA 18
 
0.1%
FABINHO 12
 
< 0.1%
ANA CLAUDIA 10
 
< 0.1%
PEDRO PAULO 9
 
< 0.1%
JUNINHO 8
 
< 0.1%
SIMONE 8
 
< 0.1%
MARCELINHO 8
 
< 0.1%
CRISTINA 8
 
< 0.1%
MARQUINHO 7
 
< 0.1%
ANA CRISTINA 7
 
< 0.1%
Other values (29681) 33004
99.7%

Length

2022-12-06T12:48:48.253201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
do 2586
 
3.5%
da 2417
 
3.3%
de 964
 
1.3%
dr 509
 
0.7%
professor 432
 
0.6%
silva 415
 
0.6%
carlos 412
 
0.6%
paulo 397
 
0.5%
marcelo 391
 
0.5%
santos 350
 
0.5%
Other values (13752) 64752
87.9%

Most occurring characters

ValueCountFrequency (%)
A 61862
13.2%
O 42697
 
9.1%
41072
 
8.8%
I 37687
 
8.0%
E 37544
 
8.0%
R 37529
 
8.0%
N 27437
 
5.8%
L 23838
 
5.1%
S 22651
 
4.8%
D 20899
 
4.5%
Other values (54) 115931
24.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 427547
91.1%
Space Separator 41072
 
8.8%
Other Punctuation 349
 
0.1%
Decimal Number 81
 
< 0.1%
Dash Punctuation 35
 
< 0.1%
Open Punctuation 29
 
< 0.1%
Close Punctuation 29
 
< 0.1%
Other Symbol 3
 
< 0.1%
Modifier Symbol 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 61862
14.5%
O 42697
10.0%
I 37687
 
8.8%
E 37544
 
8.8%
R 37529
 
8.8%
N 27437
 
6.4%
L 23838
 
5.6%
S 22651
 
5.3%
D 20899
 
4.9%
C 14880
 
3.5%
Other values (33) 100523
23.5%
Decimal Number
ValueCountFrequency (%)
0 22
27.2%
1 20
24.7%
2 14
17.3%
3 7
 
8.6%
7 6
 
7.4%
9 4
 
4.9%
4 3
 
3.7%
8 3
 
3.7%
5 2
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 342
98.0%
% 4
 
1.1%
? 1
 
0.3%
@ 1
 
0.3%
: 1
 
0.3%
Modifier Symbol
ValueCountFrequency (%)
´ 1
50.0%
` 1
50.0%
Space Separator
ValueCountFrequency (%)
41072
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Other Symbol
ValueCountFrequency (%)
° 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 427547
91.1%
Common 41600
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 61862
14.5%
O 42697
10.0%
I 37687
 
8.8%
E 37544
 
8.8%
R 37529
 
8.8%
N 27437
 
6.4%
L 23838
 
5.6%
S 22651
 
5.3%
D 20899
 
4.9%
C 14880
 
3.5%
Other values (33) 100523
23.5%
Common
ValueCountFrequency (%)
41072
98.7%
. 342
 
0.8%
- 35
 
0.1%
( 29
 
0.1%
) 29
 
0.1%
0 22
 
0.1%
1 20
 
< 0.1%
2 14
 
< 0.1%
3 7
 
< 0.1%
7 6
 
< 0.1%
Other values (11) 24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 462174
98.5%
None 6973
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 61862
13.4%
O 42697
 
9.2%
41072
 
8.9%
I 37687
 
8.2%
E 37544
 
8.1%
R 37529
 
8.1%
N 27437
 
5.9%
L 23838
 
5.2%
S 22651
 
4.9%
D 20899
 
4.5%
Other values (35) 108958
23.6%
None
ValueCountFrequency (%)
à 1841
26.4%
É 1475
21.2%
Á 1035
14.8%
Ç 735
 
10.5%
Ú 485
 
7.0%
Í 352
 
5.0%
Ó 325
 
4.7%
Ô 256
 
3.7%
Ê 253
 
3.6%
 168
 
2.4%
Other values (9) 48
 
0.7%

NM_SOCIAL_CANDIDATO
Categorical

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)15.8%
Missing33023
Missing (%)99.8%
Memory size1.3 MiB
NÃO DIVULGÁVEL
62 
SÔNIA DE ARSOLINO
 
3
INDIANARE SIQUEIRA
 
2
KAROL FERREIRA DOS SANTOS RODRIGUES
 
1
BÁRBARA SHELDON SANTANA DA SILVA
 
1
Other values (7)

Length

Max length35
Median length14
Mean length14.973684
Min length5

Characters and Unicode

Total characters1138
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)11.8%

Sample

1st rowKAROL FERREIRA DOS SANTOS RODRIGUES
2nd rowBÁRBARA SHELDON SANTANA DA SILVA
3rd rowBARBARA AIRES
4th rowLOREN ALEXSANDRE CARNEIRO
5th rowSÔNIA DE ARSOLINO

Common Values

ValueCountFrequency (%)
NÃO DIVULGÁVEL 62
 
0.2%
SÔNIA DE ARSOLINO 3
 
< 0.1%
INDIANARE SIQUEIRA 2
 
< 0.1%
KAROL FERREIRA DOS SANTOS RODRIGUES 1
 
< 0.1%
BÁRBARA SHELDON SANTANA DA SILVA 1
 
< 0.1%
BARBARA AIRES 1
 
< 0.1%
LOREN ALEXSANDRE CARNEIRO 1
 
< 0.1%
ROSA MIRANDA 1
 
< 0.1%
LARYSSA DE OLIVEIRA SILVA 1
 
< 0.1%
JOYCE RODRIGUES DA SILVA 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 33023
99.8%

Length

2022-12-06T12:48:48.353420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
não 62
37.6%
divulgável 62
37.6%
de 4
 
2.4%
sônia 3
 
1.8%
arsolino 3
 
1.8%
silva 3
 
1.8%
rodrigues 2
 
1.2%
da 2
 
1.2%
siqueira 2
 
1.2%
indianare 2
 
1.2%
Other values (20) 20
 
12.1%

Most occurring characters

ValueCountFrequency (%)
L 137
12.0%
V 128
11.2%
89
 
7.8%
I 88
 
7.7%
E 82
 
7.2%
N 80
 
7.0%
O 80
 
7.0%
D 76
 
6.7%
U 67
 
5.9%
G 64
 
5.6%
Other values (19) 247
21.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1049
92.2%
Space Separator 89
 
7.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 137
13.1%
V 128
12.2%
I 88
8.4%
E 82
7.8%
N 80
7.6%
O 80
7.6%
D 76
7.2%
U 67
 
6.4%
G 64
 
6.1%
Á 63
 
6.0%
Other values (18) 184
17.5%
Space Separator
ValueCountFrequency (%)
89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1049
92.2%
Common 89
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 137
13.1%
V 128
12.2%
I 88
8.4%
E 82
7.8%
N 80
7.6%
O 80
7.6%
D 76
7.2%
U 67
 
6.4%
G 64
 
6.1%
Á 63
 
6.0%
Other values (18) 184
17.5%
Common
ValueCountFrequency (%)
89
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1008
88.6%
None 130
 
11.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 137
13.6%
V 128
12.7%
89
8.8%
I 88
8.7%
E 82
8.1%
N 80
7.9%
O 80
7.9%
D 76
7.5%
U 67
6.6%
G 64
6.3%
Other values (14) 117
11.6%
None
ValueCountFrequency (%)
Á 63
48.5%
à 62
47.7%
Ô 3
 
2.3%
É 1
 
0.8%
Ú 1
 
0.8%

NR_CPF_CANDIDATO
Real number (ℝ)

Distinct30443
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6348934 × 1010
Minimum-4
Maximum9.9999448 × 1010
Zeros0
Zeros (%)0.0%
Negative62
Negative (%)0.2%
Memory size291.0 KiB
2022-12-06T12:48:48.469298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile7.3624374 × 108
Q15.1615372 × 109
median1.0080331 × 1010
Q34.7227766 × 1010
95-th percentile9.0671954 × 1010
Maximum9.9999448 × 1010
Range9.9999448 × 1010
Interquartile range (IQR)4.2066229 × 1010

Descriptive statistics

Standard deviation3.1090261 × 1010
Coefficient of variation (CV)1.1799438
Kurtosis-0.2946505
Mean2.6348934 × 1010
Median Absolute Deviation (MAD)7.046124 × 109
Skewness1.1433495
Sum8.7212338 × 1014
Variance9.6660436 × 1020
MonotonicityNot monotonic
2022-12-06T12:48:48.585281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4 62
 
0.2%
2122060794 5
 
< 0.1%
98271016768 4
 
< 0.1%
349929793 4
 
< 0.1%
1891690795 4
 
< 0.1%
5343393799 4
 
< 0.1%
1475189702 4
 
< 0.1%
1397326751 4
 
< 0.1%
76805905753 4
 
< 0.1%
83753176753 4
 
< 0.1%
Other values (30433) 33000
99.7%
ValueCountFrequency (%)
-4 62
0.2%
1078755 1
 
< 0.1%
2452758 1
 
< 0.1%
3496740 1
 
< 0.1%
3512703 1
 
< 0.1%
4772733 2
 
< 0.1%
6186750 1
 
< 0.1%
6616780 1
 
< 0.1%
6674712 1
 
< 0.1%
7487738 2
 
< 0.1%
ValueCountFrequency (%)
99999447791 1
< 0.1%
99997339720 1
< 0.1%
99982382772 1
< 0.1%
99975688772 1
< 0.1%
99974428734 1
< 0.1%
99969114700 1
< 0.1%
99964635753 1
< 0.1%
99962454700 1
< 0.1%
99961920678 1
< 0.1%
99961580710 1
< 0.1%

NM_EMAIL
Categorical

Distinct12989
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
NÃO DIVULGÁVEL
6546 
JANUZA.ADV@GMAIL.COM
 
285
RABELLO_JR@YAHOO.COM.BR
 
251
ELEICAO2020NOVAFRIBURGO@GMAIL.COM
 
232
ESCRITORIOPARTIDARIO2020@GMAIL.COM
 
198
Other values (12984)
25587 

Length

Max length47
Median length39
Mean length23.500076
Min length12

Characters and Unicode

Total characters777829
Distinct characters48
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12074 ?
Unique (%)36.5%

Sample

1st rowNÃO DIVULGÁVEL
2nd rowNÃO DIVULGÁVEL
3rd rowNÃO DIVULGÁVEL
4th rowNÃO DIVULGÁVEL
5th rowNÃO DIVULGÁVEL

Common Values

ValueCountFrequency (%)
NÃO DIVULGÁVEL 6546
 
19.8%
JANUZA.ADV@GMAIL.COM 285
 
0.9%
RABELLO_JR@YAHOO.COM.BR 251
 
0.8%
ELEICAO2020NOVAFRIBURGO@GMAIL.COM 232
 
0.7%
ESCRITORIOPARTIDARIO2020@GMAIL.COM 198
 
0.6%
JURIDICO.ANDREMONICA@GMAIL.COM 198
 
0.6%
THIAGO1989.TPL@GMAIL.COM 163
 
0.5%
MIRACEMA.ELEICOES2020@GMAIL.COM 124
 
0.4%
ELEICOES2020TANGUARJ@GMAIL.COM 114
 
0.3%
ELEICAOCAMPOS2020@GMAIL.COM 111
 
0.3%
Other values (12979) 24877
75.2%

Length

2022-12-06T12:48:48.707389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
não 6546
 
16.5%
divulgável 6546
 
16.5%
januza.adv@gmail.com 285
 
0.7%
rabello_jr@yahoo.com.br 251
 
0.6%
eleicao2020novafriburgo@gmail.com 232
 
0.6%
escritoriopartidario2020@gmail.com 198
 
0.5%
juridico.andremonica@gmail.com 198
 
0.5%
thiago1989.tpl@gmail.com 163
 
0.4%
miracema.eleicoes2020@gmail.com 124
 
0.3%
eleicoes2020tanguarj@gmail.com 114
 
0.3%
Other values (12980) 24988
63.0%

Most occurring characters

ValueCountFrequency (%)
O 77000
 
9.9%
A 76082
 
9.8%
I 62235
 
8.0%
M 61464
 
7.9%
L 55632
 
7.2%
C 43569
 
5.6%
E 40144
 
5.2%
. 35310
 
4.5%
R 35181
 
4.5%
G 32144
 
4.1%
Other values (38) 259068
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 672790
86.5%
Other Punctuation 61866
 
8.0%
Decimal Number 35472
 
4.6%
Space Separator 6550
 
0.8%
Connector Punctuation 915
 
0.1%
Dash Punctuation 233
 
< 0.1%
Math Symbol 2
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 77000
11.4%
A 76082
11.3%
I 62235
 
9.3%
M 61464
 
9.1%
L 55632
 
8.3%
C 43569
 
6.5%
E 40144
 
6.0%
R 35181
 
5.2%
G 32144
 
4.8%
N 24435
 
3.6%
Other values (18) 164904
24.5%
Decimal Number
ValueCountFrequency (%)
2 11349
32.0%
0 10569
29.8%
1 3859
 
10.9%
3 1738
 
4.9%
9 1632
 
4.6%
5 1518
 
4.3%
7 1417
 
4.0%
8 1162
 
3.3%
4 1116
 
3.1%
6 1112
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 35310
57.1%
@ 26553
42.9%
' 1
 
< 0.1%
/ 1
 
< 0.1%
! 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
6550
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 915
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 233
100.0%
Math Symbol
ValueCountFrequency (%)
| 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 672790
86.5%
Common 105039
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 77000
11.4%
A 76082
11.3%
I 62235
 
9.3%
M 61464
 
9.1%
L 55632
 
8.3%
C 43569
 
6.5%
E 40144
 
6.0%
R 35181
 
5.2%
G 32144
 
4.8%
N 24435
 
3.6%
Other values (18) 164904
24.5%
Common
ValueCountFrequency (%)
. 35310
33.6%
@ 26553
25.3%
2 11349
 
10.8%
0 10569
 
10.1%
6550
 
6.2%
1 3859
 
3.7%
3 1738
 
1.7%
9 1632
 
1.6%
5 1518
 
1.4%
7 1417
 
1.3%
Other values (10) 4544
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 764737
98.3%
None 13092
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 77000
 
10.1%
A 76082
 
9.9%
I 62235
 
8.1%
M 61464
 
8.0%
L 55632
 
7.3%
C 43569
 
5.7%
E 40144
 
5.2%
. 35310
 
4.6%
R 35181
 
4.6%
G 32144
 
4.2%
Other values (36) 245976
32.2%
None
ValueCountFrequency (%)
Á 6546
50.0%
à 6546
50.0%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
12
30638 
3
 
2459
1
 
2

Length

Max length2
Median length2
Mean length1.9256473
Min length1

Characters and Unicode

Total characters63737
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
12 30638
92.6%
3 2459
 
7.4%
1 2
 
< 0.1%

Length

2022-12-06T12:48:48.823503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:48.924169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
12 30638
92.6%
3 2459
 
7.4%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 30640
48.1%
2 30638
48.1%
3 2459
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63737
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30640
48.1%
2 30638
48.1%
3 2459
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 63737
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30640
48.1%
2 30638
48.1%
3 2459
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63737
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30640
48.1%
2 30638
48.1%
3 2459
 
3.9%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
APTO
30638 
INAPTO
 
2459
CADASTRADO
 
2

Length

Max length10
Median length4
Mean length4.1489471
Min length4

Characters and Unicode

Total characters137326
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAPTO
2nd rowAPTO
3rd rowAPTO
4th rowAPTO
5th rowAPTO

Common Values

ValueCountFrequency (%)
APTO 30638
92.6%
INAPTO 2459
 
7.4%
CADASTRADO 2
 
< 0.1%

Length

2022-12-06T12:48:49.002342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:49.102152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
apto 30638
92.6%
inapto 2459
 
7.4%
cadastrado 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 33103
24.1%
T 33099
24.1%
O 33099
24.1%
P 33097
24.1%
I 2459
 
1.8%
N 2459
 
1.8%
D 4
 
< 0.1%
C 2
 
< 0.1%
S 2
 
< 0.1%
R 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 137326
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 33103
24.1%
T 33099
24.1%
O 33099
24.1%
P 33097
24.1%
I 2459
 
1.8%
N 2459
 
1.8%
D 4
 
< 0.1%
C 2
 
< 0.1%
S 2
 
< 0.1%
R 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 137326
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 33103
24.1%
T 33099
24.1%
O 33099
24.1%
P 33097
24.1%
I 2459
 
1.8%
N 2459
 
1.8%
D 4
 
< 0.1%
C 2
 
< 0.1%
S 2
 
< 0.1%
R 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 33103
24.1%
T 33099
24.1%
O 33099
24.1%
P 33097
24.1%
I 2459
 
1.8%
N 2459
 
1.8%
D 4
 
< 0.1%
C 2
 
< 0.1%
S 2
 
< 0.1%
R 2
 
< 0.1%

CD_DETALHE_SITUACAO_CAND
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7458231
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.0 KiB
2022-12-06T12:48:49.171213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile13
Maximum20
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7574881
Coefficient of variation (CV)1.0042483
Kurtosis12.122339
Mean2.7458231
Median Absolute Deviation (MAD)0
Skewness3.6944171
Sum90884
Variance7.6037407
MonotonicityNot monotonic
2022-12-06T12:48:49.255865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 30466
92.0%
14 1572
 
4.7%
6 621
 
1.9%
13 138
 
0.4%
4 132
 
0.4%
10 107
 
0.3%
18 20
 
0.1%
16 14
 
< 0.1%
5 13
 
< 0.1%
7 8
 
< 0.1%
Other values (3) 8
 
< 0.1%
ValueCountFrequency (%)
2 30466
92.0%
4 132
 
0.4%
5 13
 
< 0.1%
6 621
 
1.9%
7 8
 
< 0.1%
8 2
 
< 0.1%
10 107
 
0.3%
13 138
 
0.4%
14 1572
 
4.7%
16 14
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18 20
 
0.1%
17 5
 
< 0.1%
16 14
 
< 0.1%
14 1572
4.7%
13 138
 
0.4%
10 107
 
0.3%
8 2
 
< 0.1%
7 8
 
< 0.1%
6 621
 
1.9%
Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
DEFERIDO
30466 
INDEFERIDO
 
1572
RENÚNCIA
 
621
PEDIDO NÃO CONHECIDO
 
138
INDEFERIDO COM RECURSO
 
132
Other values (8)
 
170

Length

Max length32
Median length8
Mean length8.2133599
Min length7

Characters and Unicode

Total characters271854
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDEFERIDO
2nd rowDEFERIDO
3rd rowDEFERIDO
4th rowDEFERIDO
5th rowDEFERIDO

Common Values

ValueCountFrequency (%)
DEFERIDO 30466
92.0%
INDEFERIDO 1572
 
4.7%
RENÚNCIA 621
 
1.9%
PEDIDO NÃO CONHECIDO 138
 
0.4%
INDEFERIDO COM RECURSO 132
 
0.4%
CASSADO 107
 
0.3%
CASSADO COM RECURSO 20
 
0.1%
DEFERIDO COM RECURSO 14
 
< 0.1%
CANCELADO 13
 
< 0.1%
FALECIDO 8
 
< 0.1%
Other values (3) 8
 
< 0.1%

Length

2022-12-06T12:48:49.356221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
deferido 30480
90.4%
indeferido 1704
 
5.1%
renúncia 621
 
1.8%
com 167
 
0.5%
recurso 167
 
0.5%
pedido 139
 
0.4%
não 139
 
0.4%
conhecido 139
 
0.4%
cassado 127
 
0.4%
cancelado 13
 
< 0.1%
Other values (5) 27
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 65482
24.1%
D 64947
23.9%
I 34795
12.8%
O 33231
12.2%
R 33141
12.2%
F 32192
11.8%
N 3256
 
1.2%
C 1394
 
0.5%
A 922
 
0.3%
624
 
0.2%
Other values (11) 1870
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 271230
99.8%
Space Separator 624
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 65482
24.1%
D 64947
23.9%
I 34795
12.8%
O 33231
12.3%
R 33141
12.2%
F 32192
11.9%
N 3256
 
1.2%
C 1394
 
0.5%
A 922
 
0.3%
Ú 621
 
0.2%
Other values (10) 1249
 
0.5%
Space Separator
ValueCountFrequency (%)
624
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 271230
99.8%
Common 624
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 65482
24.1%
D 64947
23.9%
I 34795
12.8%
O 33231
12.3%
R 33141
12.2%
F 32192
11.9%
N 3256
 
1.2%
C 1394
 
0.5%
A 922
 
0.3%
Ú 621
 
0.2%
Other values (10) 1249
 
0.5%
Common
ValueCountFrequency (%)
624
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271094
99.7%
None 760
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 65482
24.2%
D 64947
24.0%
I 34795
12.8%
O 33231
12.3%
R 33141
12.2%
F 32192
11.9%
N 3256
 
1.2%
C 1394
 
0.5%
A 922
 
0.3%
624
 
0.2%
Other values (9) 1110
 
0.4%
None
ValueCountFrequency (%)
Ú 621
81.7%
à 139
 
18.3%

TP_AGREMIACAO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
PARTIDO ISOLADO
30866 
COLIGAÇÃO
 
1899
FEDERAÇÃO
 
334

Length

Max length15
Median length15
Mean length14.595214
Min length9

Characters and Unicode

Total characters483087
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTIDO ISOLADO
2nd rowCOLIGAÇÃO
3rd rowPARTIDO ISOLADO
4th rowPARTIDO ISOLADO
5th rowPARTIDO ISOLADO

Common Values

ValueCountFrequency (%)
PARTIDO ISOLADO 30866
93.3%
COLIGAÇÃO 1899
 
5.7%
FEDERAÇÃO 334
 
1.0%

Length

2022-12-06T12:48:49.440542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:49.540885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
partido 30866
48.3%
isolado 30866
48.3%
coligação 1899
 
3.0%
federação 334
 
0.5%

Most occurring characters

ValueCountFrequency (%)
O 96730
20.0%
A 63965
13.2%
I 63631
13.2%
D 62066
12.8%
L 32765
 
6.8%
R 31200
 
6.5%
P 30866
 
6.4%
T 30866
 
6.4%
30866
 
6.4%
S 30866
 
6.4%
Other values (6) 9266
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 452221
93.6%
Space Separator 30866
 
6.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 96730
21.4%
A 63965
14.1%
I 63631
14.1%
D 62066
13.7%
L 32765
 
7.2%
R 31200
 
6.9%
P 30866
 
6.8%
T 30866
 
6.8%
S 30866
 
6.8%
Ç 2233
 
0.5%
Other values (5) 7033
 
1.6%
Space Separator
ValueCountFrequency (%)
30866
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 452221
93.6%
Common 30866
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 96730
21.4%
A 63965
14.1%
I 63631
14.1%
D 62066
13.7%
L 32765
 
7.2%
R 31200
 
6.9%
P 30866
 
6.8%
T 30866
 
6.8%
S 30866
 
6.8%
Ç 2233
 
0.5%
Other values (5) 7033
 
1.6%
Common
ValueCountFrequency (%)
30866
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 478621
99.1%
None 4466
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 96730
20.2%
A 63965
13.4%
I 63631
13.3%
D 62066
13.0%
L 32765
 
6.8%
R 31200
 
6.5%
P 30866
 
6.4%
T 30866
 
6.4%
30866
 
6.4%
S 30866
 
6.4%
Other values (4) 4800
 
1.0%
None
ValueCountFrequency (%)
Ç 2233
50.0%
à 2233
50.0%

NR_PARTIDO
Real number (ℝ)

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.061815
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.0 KiB
2022-12-06T12:48:49.625574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q117
median27
Q350
95-th percentile77
Maximum90
Range80
Interquartile range (IQR)33

Descriptive statistics

Standard deviation22.054048
Coefficient of variation (CV)0.64747132
Kurtosis-0.071989894
Mean34.061815
Median Absolute Deviation (MAD)13
Skewness0.96560636
Sum1127412
Variance486.38104
MonotonicityNot monotonic
2022-12-06T12:48:49.725884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
10 1772
 
5.4%
77 1641
 
5.0%
55 1578
 
4.8%
22 1498
 
4.5%
25 1474
 
4.5%
20 1468
 
4.4%
11 1454
 
4.4%
17 1403
 
4.2%
15 1358
 
4.1%
51 1348
 
4.1%
Other values (26) 18105
54.7%
ValueCountFrequency (%)
10 1772
5.4%
11 1454
4.4%
12 1344
4.1%
13 927
2.8%
14 1094
3.3%
15 1358
4.1%
16 54
 
0.2%
17 1403
4.2%
18 396
 
1.2%
19 1048
3.2%
ValueCountFrequency (%)
90 1207
3.6%
80 21
 
0.1%
77 1641
5.0%
70 1277
3.9%
65 685
2.1%
55 1578
4.8%
54 81
 
0.2%
51 1348
4.1%
50 568
 
1.7%
45 1120
3.4%

SG_PARTIDO
Categorical

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
SOLIDARIEDADE
 
1641
REPUBLICANOS
 
1610
PSD
 
1578
DEM
 
1474
PSC
 
1468
Other values (36)
25328 

Length

Max length13
Median length12
Mean length4.5483851
Min length2

Characters and Unicode

Total characters150547
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPP
2nd rowPTB
3rd rowPRP
4th rowPSC
5th rowPSD

Common Values

ValueCountFrequency (%)
SOLIDARIEDADE 1641
 
5.0%
REPUBLICANOS 1610
 
4.9%
PSD 1578
 
4.8%
DEM 1474
 
4.5%
PSC 1468
 
4.4%
PP 1454
 
4.4%
PSL 1403
 
4.2%
PL 1394
 
4.2%
MDB 1358
 
4.1%
PATRIOTA 1348
 
4.1%
Other values (31) 18371
55.5%

Length

2022-12-06T12:48:49.826241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
solidariedade 1641
 
4.8%
republicanos 1610
 
4.7%
psd 1578
 
4.6%
dem 1474
 
4.3%
psc 1468
 
4.3%
pp 1454
 
4.2%
psl 1403
 
4.1%
pl 1394
 
4.0%
mdb 1358
 
3.9%
patriota 1348
 
3.9%
Other values (33) 19741
57.3%

Most occurring characters

ValueCountFrequency (%)
P 26214
17.4%
D 16444
10.9%
A 13532
9.0%
S 11674
 
7.8%
E 9483
 
6.3%
T 9418
 
6.3%
B 9113
 
6.1%
I 8664
 
5.8%
R 7971
 
5.3%
O 7900
 
5.2%
Other values (12) 30134
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 147807
98.2%
Space Separator 1370
 
0.9%
Lowercase Letter 1370
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 26214
17.7%
D 16444
11.1%
A 13532
9.2%
S 11674
7.9%
E 9483
 
6.4%
T 9418
 
6.4%
B 9113
 
6.2%
I 8664
 
5.9%
R 7971
 
5.4%
O 7900
 
5.3%
Other values (9) 27394
18.5%
Lowercase Letter
ValueCountFrequency (%)
d 685
50.0%
o 685
50.0%
Space Separator
ValueCountFrequency (%)
1370
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 149177
99.1%
Common 1370
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 26214
17.6%
D 16444
11.0%
A 13532
9.1%
S 11674
7.8%
E 9483
 
6.4%
T 9418
 
6.3%
B 9113
 
6.1%
I 8664
 
5.8%
R 7971
 
5.3%
O 7900
 
5.3%
Other values (11) 28764
19.3%
Common
ValueCountFrequency (%)
1370
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150421
99.9%
None 126
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 26214
17.4%
D 16444
10.9%
A 13532
9.0%
S 11674
 
7.8%
E 9483
 
6.3%
T 9418
 
6.3%
B 9113
 
6.1%
I 8664
 
5.8%
R 7971
 
5.3%
O 7900
 
5.3%
Other values (11) 30008
19.9%
None
ValueCountFrequency (%)
à 126
100.0%

NM_PARTIDO
Categorical

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
SOLIDARIEDADE
 
1641
REPUBLICANOS
 
1610
PARTIDO SOCIAL DEMOCRÁTICO
 
1578
DEMOCRATAS
 
1474
PARTIDO SOCIAL CRISTÃO
 
1468
Other values (36)
25328 

Length

Max length46
Median length30
Mean length21.380253
Min length4

Characters and Unicode

Total characters707665
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPROGRESSISTAS
2nd rowPARTIDO TRABALHISTA BRASILEIRO
3rd rowPARTIDO REPUBLICANO PROGRESSISTA
4th rowPARTIDO SOCIAL CRISTÃO
5th rowPARTIDO SOCIAL DEMOCRÁTICO

Common Values

ValueCountFrequency (%)
SOLIDARIEDADE 1641
 
5.0%
REPUBLICANOS 1610
 
4.9%
PARTIDO SOCIAL DEMOCRÁTICO 1578
 
4.8%
DEMOCRATAS 1474
 
4.5%
PARTIDO SOCIAL CRISTÃO 1468
 
4.4%
PROGRESSISTAS 1454
 
4.4%
PARTIDO SOCIAL LIBERAL 1403
 
4.2%
PARTIDO LIBERAL 1394
 
4.2%
MOVIMENTO DEMOCRÁTICO BRASILEIRO 1358
 
4.1%
PATRIOTA 1348
 
4.1%
Other values (31) 18371
55.5%

Length

2022-12-06T12:48:49.942421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
partido 19121
23.2%
social 6776
 
8.2%
brasileiro 4650
 
5.6%
trabalhista 4464
 
5.4%
da 4418
 
5.4%
democrático 4280
 
5.2%
liberal 2797
 
3.4%
cristão 2284
 
2.8%
brasileira 2168
 
2.6%
democracia 2139
 
2.6%
Other values (41) 29475
35.7%

Most occurring characters

ValueCountFrequency (%)
A 92161
13.0%
I 78664
11.1%
R 70470
10.0%
O 69555
9.8%
49473
 
7.0%
D 48286
 
6.8%
T 47705
 
6.7%
S 41098
 
5.8%
E 39016
 
5.5%
L 35601
 
5.0%
Other values (15) 135636
19.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 658192
93.0%
Space Separator 49473
 
7.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 92161
14.0%
I 78664
12.0%
R 70470
10.7%
O 69555
10.6%
D 48286
7.3%
T 47705
7.2%
S 41098
 
6.2%
E 39016
 
5.9%
L 35601
 
5.4%
C 31753
 
4.8%
Other values (14) 103883
15.8%
Space Separator
ValueCountFrequency (%)
49473
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 658192
93.0%
Common 49473
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 92161
14.0%
I 78664
12.0%
R 70470
10.7%
O 69555
10.6%
D 48286
7.3%
T 47705
7.2%
S 41098
 
6.2%
E 39016
 
5.9%
L 35601
 
5.4%
C 31753
 
4.8%
Other values (14) 103883
15.8%
Common
ValueCountFrequency (%)
49473
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 698277
98.7%
None 9388
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 92161
13.2%
I 78664
11.3%
R 70470
10.1%
O 69555
10.0%
49473
 
7.1%
D 48286
 
6.9%
T 47705
 
6.8%
S 41098
 
5.9%
E 39016
 
5.6%
L 35601
 
5.1%
Other values (11) 126248
18.1%
None
ValueCountFrequency (%)
Á 4411
47.0%
à 4151
44.2%
Ç 722
 
7.7%
Ú 104
 
1.1%

SQ_COLIGACAO
Real number (ℝ)

Distinct2159
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9000023 × 1011
Minimum1.9000005 × 1011
Maximum1.9000169 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.0 KiB
2022-12-06T12:48:50.058388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.9000005 × 1011
5-th percentile1.9000005 × 1011
Q11.9000007 × 1011
median1.9000011 × 1011
Q31.9000014 × 1011
95-th percentile1.9000168 × 1011
Maximum1.9000169 × 1011
Range1635422
Interquartile range (IQR)66547

Descriptive statistics

Standard deviation441736.08
Coefficient of variation (CV)2.3249239 × 10-6
Kurtosis6.7604027
Mean1.9000023 × 1011
Median Absolute Deviation (MAD)33484
Skewness2.9449198
Sum6.2888177 × 1015
Variance1.9513077 × 1011
MonotonicityNot monotonic
2022-12-06T12:48:50.174354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190000050661 176
 
0.5%
190000050390 176
 
0.5%
190000050675 174
 
0.5%
190000050173 167
 
0.5%
190000050720 159
 
0.5%
190000050208 157
 
0.5%
190000050716 155
 
0.5%
190000050610 134
 
0.4%
190000050058 111
 
0.3%
190000050392 110
 
0.3%
Other values (2149) 31580
95.4%
ValueCountFrequency (%)
190000050043 76
0.2%
190000050058 111
0.3%
190000050062 49
 
0.1%
190000050092 106
0.3%
190000050094 69
0.2%
190000050104 74
0.2%
190000050120 70
0.2%
190000050173 167
0.5%
190000050195 73
0.2%
190000050205 70
0.2%
ValueCountFrequency (%)
190001685465 48
0.1%
190001685462 4
 
< 0.1%
190001685461 2
 
< 0.1%
190001685459 3
 
< 0.1%
190001685458 2
 
< 0.1%
190001685428 4
 
< 0.1%
190001685426 67
0.2%
190001685397 71
0.2%
190001685220 6
 
< 0.1%
190001685219 7
 
< 0.1%

NM_COLIGACAO
Categorical

Distinct350
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
PARTIDO ISOLADO
30866 
FEDERAÇÃO
 
334
Rio Sustentável
 
174
MUDAR É POSSÍVEL
 
111
O POVO NO PODER
 
106
Other values (345)
 
1508

Length

Max length83
Median length15
Mean length15.225928
Min length7

Characters and Unicode

Total characters503963
Distinct characters79
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTIDO ISOLADO
2nd rowTRABALHAR PARA MUDAR
3rd rowPARTIDO ISOLADO
4th rowPARTIDO ISOLADO
5th rowPARTIDO ISOLADO

Common Values

ValueCountFrequency (%)
PARTIDO ISOLADO 30866
93.3%
FEDERAÇÃO 334
 
1.0%
Rio Sustentável 174
 
0.5%
MUDAR É POSSÍVEL 111
 
0.3%
O POVO NO PODER 106
 
0.3%
TRABALHAR PARA MUDAR 106
 
0.3%
O RIO SEM CRISE 96
 
0.3%
RENOVAR PARA MUDAR 77
 
0.2%
O RIO QUER PAZ 73
 
0.2%
JUNTOS PELO RIO 70
 
0.2%
Other values (340) 1086
 
3.3%

Length

2022-12-06T12:48:50.312450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
partido 30866
44.5%
isolado 30866
44.5%
rio 661
 
1.0%
o 413
 
0.6%
federação 334
 
0.5%
mudar 315
 
0.5%
para 279
 
0.4%
por 250
 
0.4%
povo 175
 
0.3%
sustentável 174
 
0.3%
Other values (389) 4964
 
7.2%

Most occurring characters

ValueCountFrequency (%)
O 96637
19.2%
A 65314
13.0%
I 63303
12.6%
D 63143
12.5%
36234
 
7.2%
R 34463
 
6.8%
S 32522
 
6.5%
P 32490
 
6.4%
T 31933
 
6.3%
L 31627
 
6.3%
Other values (69) 16297
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 462580
91.8%
Space Separator 36234
 
7.2%
Lowercase Letter 4872
 
1.0%
Other Punctuation 221
 
< 0.1%
Dash Punctuation 38
 
< 0.1%
Decimal Number 12
 
< 0.1%
Modifier Symbol 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 96637
20.9%
A 65314
14.1%
I 63303
13.7%
D 63143
13.7%
R 34463
 
7.5%
S 32522
 
7.0%
P 32490
 
7.0%
T 31933
 
6.9%
L 31627
 
6.8%
E 2859
 
0.6%
Other values (23) 8289
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
e 597
12.3%
o 581
11.9%
t 461
9.5%
a 457
9.4%
i 334
 
6.9%
n 326
 
6.7%
s 301
 
6.2%
r 286
 
5.9%
l 265
 
5.4%
u 250
 
5.1%
Other values (22) 1014
20.8%
Other Punctuation
ValueCountFrequency (%)
, 92
41.6%
/ 75
33.9%
! 24
 
10.9%
¿ 12
 
5.4%
. 10
 
4.5%
: 8
 
3.6%
Decimal Number
ValueCountFrequency (%)
0 6
50.0%
2 4
33.3%
1 2
 
16.7%
Space Separator
ValueCountFrequency (%)
36234
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 467452
92.8%
Common 36511
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 96637
20.7%
A 65314
14.0%
I 63303
13.5%
D 63143
13.5%
R 34463
 
7.4%
S 32522
 
7.0%
P 32490
 
7.0%
T 31933
 
6.8%
L 31627
 
6.8%
E 2859
 
0.6%
Other values (55) 13161
 
2.8%
Common
ValueCountFrequency (%)
36234
99.2%
, 92
 
0.3%
/ 75
 
0.2%
- 38
 
0.1%
! 24
 
0.1%
¿ 12
 
< 0.1%
. 10
 
< 0.1%
: 8
 
< 0.1%
0 6
 
< 0.1%
2 4
 
< 0.1%
Other values (4) 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 502298
99.7%
None 1665
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 96637
19.2%
A 65314
13.0%
I 63303
12.6%
D 63143
12.6%
36234
 
7.2%
R 34463
 
6.9%
S 32522
 
6.5%
P 32490
 
6.5%
T 31933
 
6.4%
L 31627
 
6.3%
Other values (50) 14632
 
2.9%
None
ValueCountFrequency (%)
Ç 492
29.5%
à 464
27.9%
É 184
 
11.1%
á 174
 
10.5%
Í 135
 
8.1%
ç 57
 
3.4%
ã 44
 
2.6%
Ó 35
 
2.1%
Ê 20
 
1.2%
ó 16
 
1.0%
Other values (9) 44
 
2.6%
Distinct364
Distinct (%)1.1%
Missing43
Missing (%)0.1%
Memory size2.0 MiB
REPUBLICANOS
 
1561
PSD
 
1532
SOLIDARIEDADE
 
1496
PP
 
1404
DEM
 
1398
Other values (359)
25665 

Length

Max length136
Median length115
Mean length5.4220111
Min length2

Characters and Unicode

Total characters179230
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPP
2nd rowSOLIDARIEDADE / PTB
3rd rowPRP
4th rowPSC
5th rowPSD

Common Values

ValueCountFrequency (%)
REPUBLICANOS 1561
 
4.7%
PSD 1532
 
4.6%
SOLIDARIEDADE 1496
 
4.5%
PP 1404
 
4.2%
DEM 1398
 
4.2%
PSL 1359
 
4.1%
PSC 1351
 
4.1%
PL 1346
 
4.1%
PATRIOTA 1338
 
4.0%
MDB 1293
 
3.9%
Other values (354) 18978
57.3%

Length

2022-12-06T12:48:50.444027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3640
 
8.7%
solidariedade 1835
 
4.4%
republicanos 1698
 
4.0%
psd 1661
 
3.9%
pp 1631
 
3.9%
dem 1625
 
3.9%
psc 1565
 
3.7%
mdb 1518
 
3.6%
pl 1501
 
3.6%
psl 1486
 
3.5%
Other values (38) 23906
56.8%

Most occurring characters

ValueCountFrequency (%)
P 29640
16.5%
D 18461
10.3%
A 14905
 
8.3%
S 12912
 
7.2%
E 10535
 
5.9%
B 10386
 
5.8%
T 10280
 
5.7%
I 9561
 
5.3%
9010
 
5.0%
R 8726
 
4.9%
Other values (13) 44814
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 164386
91.7%
Space Separator 9010
 
5.0%
Other Punctuation 4104
 
2.3%
Lowercase Letter 1730
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 29640
18.0%
D 18461
11.2%
A 14905
9.1%
S 12912
7.9%
E 10535
 
6.4%
B 10386
 
6.3%
T 10280
 
6.3%
I 9561
 
5.8%
R 8726
 
5.3%
O 8606
 
5.2%
Other values (9) 30374
18.5%
Lowercase Letter
ValueCountFrequency (%)
d 865
50.0%
o 865
50.0%
Space Separator
ValueCountFrequency (%)
9010
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 4104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 166116
92.7%
Common 13114
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 29640
17.8%
D 18461
11.1%
A 14905
9.0%
S 12912
7.8%
E 10535
 
6.3%
B 10386
 
6.3%
T 10280
 
6.2%
I 9561
 
5.8%
R 8726
 
5.3%
O 8606
 
5.2%
Other values (11) 32104
19.3%
Common
ValueCountFrequency (%)
9010
68.7%
/ 4104
31.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179102
99.9%
None 128
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 29640
16.5%
D 18461
10.3%
A 14905
 
8.3%
S 12912
 
7.2%
E 10535
 
5.9%
B 10386
 
5.8%
T 10280
 
5.7%
I 9561
 
5.3%
9010
 
5.0%
R 8726
 
4.9%
Other values (12) 44686
25.0%
None
ValueCountFrequency (%)
à 128
100.0%

CD_NACIONALIDADE
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
1
32839 
2
 
185
-4
 
62
3
 
10
4
 
3

Length

Max length2
Median length1
Mean length1.0018732
Min length1

Characters and Unicode

Total characters33161
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 32839
99.2%
2 185
 
0.6%
-4 62
 
0.2%
3 10
 
< 0.1%
4 3
 
< 0.1%

Length

2022-12-06T12:48:50.544206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:50.644215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 32839
99.2%
2 185
 
0.6%
4 65
 
0.2%
3 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 32839
99.0%
2 185
 
0.6%
4 65
 
0.2%
- 62
 
0.2%
3 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33099
99.8%
Dash Punctuation 62
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 32839
99.2%
2 185
 
0.6%
4 65
 
0.2%
3 10
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 32839
99.0%
2 185
 
0.6%
4 65
 
0.2%
- 62
 
0.2%
3 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 32839
99.0%
2 185
 
0.6%
4 65
 
0.2%
- 62
 
0.2%
3 10
 
< 0.1%

DS_NACIONALIDADE
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
BRASILEIRA NATA
32839 
BRASILEIRA (NATURALIZADA)
 
185
NÃO DIVULGÁVEL
 
62
PORTUGUESA COM IGUALDADE DE DIREITOS
 
10
ESTRANGEIRO
 
3

Length

Max length36
Median length15
Mean length15.060002
Min length11

Characters and Unicode

Total characters498471
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRASILEIRA NATA
2nd rowBRASILEIRA NATA
3rd rowBRASILEIRA NATA
4th rowBRASILEIRA NATA
5th rowBRASILEIRA NATA

Common Values

ValueCountFrequency (%)
BRASILEIRA NATA 32839
99.2%
BRASILEIRA (NATURALIZADA) 185
 
0.6%
NÃO DIVULGÁVEL 62
 
0.2%
PORTUGUESA COM IGUALDADE DE DIREITOS 10
 
< 0.1%
ESTRANGEIRO 3
 
< 0.1%

Length

2022-12-06T12:48:50.728845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:50.829206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
brasileira 33024
49.9%
nata 32839
49.6%
naturalizada 185
 
0.3%
não 62
 
0.1%
divulgável 62
 
0.1%
portuguesa 10
 
< 0.1%
com 10
 
< 0.1%
igualdade 10
 
< 0.1%
de 10
 
< 0.1%
direitos 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 132499
26.6%
I 66328
13.3%
R 66259
13.3%
L 33343
 
6.7%
E 33132
 
6.6%
33126
 
6.6%
N 33089
 
6.6%
S 33047
 
6.6%
T 33047
 
6.6%
B 33024
 
6.6%
Other values (13) 1577
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 464975
93.3%
Space Separator 33126
 
6.6%
Open Punctuation 185
 
< 0.1%
Close Punctuation 185
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 132499
28.5%
I 66328
14.3%
R 66259
14.3%
L 33343
 
7.2%
E 33132
 
7.1%
N 33089
 
7.1%
S 33047
 
7.1%
T 33047
 
7.1%
B 33024
 
7.1%
D 287
 
0.1%
Other values (10) 920
 
0.2%
Space Separator
ValueCountFrequency (%)
33126
100.0%
Open Punctuation
ValueCountFrequency (%)
( 185
100.0%
Close Punctuation
ValueCountFrequency (%)
) 185
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 464975
93.3%
Common 33496
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 132499
28.5%
I 66328
14.3%
R 66259
14.3%
L 33343
 
7.2%
E 33132
 
7.1%
N 33089
 
7.1%
S 33047
 
7.1%
T 33047
 
7.1%
B 33024
 
7.1%
D 287
 
0.1%
Other values (10) 920
 
0.2%
Common
ValueCountFrequency (%)
33126
98.9%
( 185
 
0.6%
) 185
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 498347
> 99.9%
None 124
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 132499
26.6%
I 66328
13.3%
R 66259
13.3%
L 33343
 
6.7%
E 33132
 
6.6%
33126
 
6.6%
N 33089
 
6.6%
S 33047
 
6.6%
T 33047
 
6.6%
B 33024
 
6.6%
Other values (11) 1453
 
0.3%
None
ValueCountFrequency (%)
à 62
50.0%
Á 62
50.0%

SG_UF_NASCIMENTO
Categorical

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
RJ
29704 
MG
 
963
SP
 
372
BA
 
295
ES
 
271
Other values (23)
 
1494

Length

Max length14
Median length2
Mean length2.022478
Min length2

Characters and Unicode

Total characters66942
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRJ
2nd rowRJ
3rd rowRJ
4th rowRJ
5th rowRJ

Common Values

ValueCountFrequency (%)
RJ 29704
89.7%
MG 963
 
2.9%
SP 372
 
1.1%
BA 295
 
0.9%
ES 271
 
0.8%
PB 260
 
0.8%
PE 235
 
0.7%
CE 163
 
0.5%
MA 111
 
0.3%
PA 95
 
0.3%
Other values (18) 630
 
1.9%

Length

2022-12-06T12:48:50.929516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rj 29704
89.6%
mg 963
 
2.9%
sp 372
 
1.1%
ba 295
 
0.9%
es 271
 
0.8%
pb 260
 
0.8%
pe 235
 
0.7%
ce 163
 
0.5%
ma 111
 
0.3%
pa 95
 
0.3%
Other values (19) 692
 
2.1%

Most occurring characters

ValueCountFrequency (%)
R 29955
44.7%
J 29704
44.4%
M 1123
 
1.7%
P 1078
 
1.6%
G 985
 
1.5%
S 799
 
1.2%
E 706
 
1.1%
A 585
 
0.9%
B 555
 
0.8%
C 183
 
0.3%
Other values (19) 1269
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 66136
98.8%
Lowercase Letter 744
 
1.1%
Space Separator 62
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 29955
45.3%
J 29704
44.9%
M 1123
 
1.7%
P 1078
 
1.6%
G 985
 
1.5%
S 799
 
1.2%
E 706
 
1.1%
A 585
 
0.9%
B 555
 
0.8%
C 183
 
0.3%
Other values (8) 463
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
l 124
16.7%
v 124
16.7%
u 62
8.3%
e 62
8.3%
á 62
8.3%
g 62
8.3%
d 62
8.3%
i 62
8.3%
o 62
8.3%
ã 62
8.3%
Space Separator
ValueCountFrequency (%)
62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66880
99.9%
Common 62
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 29955
44.8%
J 29704
44.4%
M 1123
 
1.7%
P 1078
 
1.6%
G 985
 
1.5%
S 799
 
1.2%
E 706
 
1.1%
A 585
 
0.9%
B 555
 
0.8%
C 183
 
0.3%
Other values (18) 1207
 
1.8%
Common
ValueCountFrequency (%)
62
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66818
99.8%
None 124
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 29955
44.8%
J 29704
44.5%
M 1123
 
1.7%
P 1078
 
1.6%
G 985
 
1.5%
S 799
 
1.2%
E 706
 
1.1%
A 585
 
0.9%
B 555
 
0.8%
C 183
 
0.3%
Other values (17) 1145
 
1.7%
None
ValueCountFrequency (%)
á 62
50.0%
ã 62
50.0%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
-3
33099 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters66198
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-3
2nd row-3
3rd row-3
4th row-3
5th row-3

Common Values

ValueCountFrequency (%)
-3 33099
100.0%

Length

2022-12-06T12:48:51.029789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:51.114455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
3 33099
100.0%

Most occurring characters

ValueCountFrequency (%)
- 33099
50.0%
3 33099
50.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 33099
50.0%
Decimal Number 33099
50.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
- 33099
100.0%
Decimal Number
ValueCountFrequency (%)
3 33099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66198
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 33099
50.0%
3 33099
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 33099
50.0%
3 33099
50.0%
Distinct1231
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
RIO DE JANEIRO
9883 
CAMPOS DOS GOYTACAZES
 
1188
SÃO GONÇALO
 
1052
DUQUE DE CAXIAS
 
942
NITERÓI
 
931
Other values (1226)
19103 

Length

Max length30
Median length28
Mean length12.294178
Min length2

Characters and Unicode

Total characters406925
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique653 ?
Unique (%)2.0%

Sample

1st rowNITEROI
2nd rowRIO DE JANEIRO
3rd rowRIO DE JANEIRO
4th rowNOVA IGUAÇU
5th rowDUQUE DE CAXIAS

Common Values

ValueCountFrequency (%)
RIO DE JANEIRO 9883
29.9%
CAMPOS DOS GOYTACAZES 1188
 
3.6%
SÃO GONÇALO 1052
 
3.2%
DUQUE DE CAXIAS 942
 
2.8%
NITERÓI 931
 
2.8%
NOVA IGUAÇU 840
 
2.5%
SÃO JOÃO DE MERITI 681
 
2.1%
VOLTA REDONDA 618
 
1.9%
PETRÓPOLIS 545
 
1.6%
NOVA FRIBURGO 495
 
1.5%
Other values (1221) 15924
48.1%

Length

2022-12-06T12:48:51.192623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 12804
17.2%
rio 10456
 
14.1%
janeiro 9893
 
13.3%
são 2848
 
3.8%
dos 1521
 
2.0%
nova 1391
 
1.9%
do 1309
 
1.8%
campos 1211
 
1.6%
goytacazes 1191
 
1.6%
gonçalo 1068
 
1.4%
Other values (1207) 30547
41.1%

Most occurring characters

ValueCountFrequency (%)
A 47149
11.6%
O 46603
11.5%
41144
10.1%
I 38565
9.5%
R 38206
9.4%
E 37264
9.2%
D 21664
 
5.3%
N 20566
 
5.1%
S 17173
 
4.2%
J 11834
 
2.9%
Other values (35) 86757
21.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 365716
89.9%
Space Separator 41144
 
10.1%
Dash Punctuation 54
 
< 0.1%
Decimal Number 11
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 47149
12.9%
O 46603
12.7%
I 38565
10.5%
R 38206
10.4%
E 37264
10.2%
D 21664
 
5.9%
N 20566
 
5.6%
S 17173
 
4.7%
J 11834
 
3.2%
T 9803
 
2.7%
Other values (27) 76889
21.0%
Decimal Number
ValueCountFrequency (%)
5 3
27.3%
0 2
18.2%
7 2
18.2%
6 2
18.2%
3 1
 
9.1%
4 1
 
9.1%
Space Separator
ValueCountFrequency (%)
41144
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 365716
89.9%
Common 41209
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 47149
12.9%
O 46603
12.7%
I 38565
10.5%
R 38206
10.4%
E 37264
10.2%
D 21664
 
5.9%
N 20566
 
5.6%
S 17173
 
4.7%
J 11834
 
3.2%
T 9803
 
2.7%
Other values (27) 76889
21.0%
Common
ValueCountFrequency (%)
41144
99.8%
- 54
 
0.1%
5 3
 
< 0.1%
0 2
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 393601
96.7%
None 13324
 
3.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 47149
12.0%
O 46603
11.8%
41144
10.5%
I 38565
9.8%
R 38206
9.7%
E 37264
9.5%
D 21664
 
5.5%
N 20566
 
5.2%
S 17173
 
4.4%
J 11834
 
3.0%
Other values (24) 73433
18.7%
None
ValueCountFrequency (%)
à 4159
31.2%
Ç 2392
18.0%
Ó 2196
16.5%
É 1609
 
12.1%
Í 1440
 
10.8%
Á 565
 
4.2%
Ê 477
 
3.6%
Ô 259
 
1.9%
Ú 179
 
1.3%
 44
 
0.3%

DT_NASCIMENTO
Categorical

Distinct13762
Distinct (%)41.7%
Missing62
Missing (%)0.2%
Memory size2.1 MiB
19/09/1970
 
13
01/06/1979
 
12
30/03/1971
 
12
30/06/1978
 
11
03/04/1980
 
11
Other values (13757)
32978 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters330370
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5154 ?
Unique (%)15.6%

Sample

1st row06/10/1959
2nd row01/05/1971
3rd row08/10/1971
4th row11/08/1970
5th row13/07/1974

Common Values

ValueCountFrequency (%)
19/09/1970 13
 
< 0.1%
01/06/1979 12
 
< 0.1%
30/03/1971 12
 
< 0.1%
30/06/1978 11
 
< 0.1%
03/04/1980 11
 
< 0.1%
18/05/1972 10
 
< 0.1%
17/01/1966 10
 
< 0.1%
13/07/1974 10
 
< 0.1%
03/05/1975 10
 
< 0.1%
06/09/1967 10
 
< 0.1%
Other values (13752) 32928
99.5%
(Missing) 62
 
0.2%

Length

2022-12-06T12:48:51.292905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19/09/1970 13
 
< 0.1%
30/03/1971 12
 
< 0.1%
01/06/1979 12
 
< 0.1%
30/06/1978 11
 
< 0.1%
03/04/1980 11
 
< 0.1%
15/02/1975 10
 
< 0.1%
29/04/1966 10
 
< 0.1%
12/02/1972 10
 
< 0.1%
30/11/1971 10
 
< 0.1%
22/04/1970 10
 
< 0.1%
Other values (13752) 32928
99.7%

Most occurring characters

ValueCountFrequency (%)
/ 66074
20.0%
1 64004
19.4%
0 44773
13.6%
9 44184
13.4%
2 22593
 
6.8%
7 20084
 
6.1%
6 18223
 
5.5%
8 16134
 
4.9%
5 13180
 
4.0%
3 11011
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 264296
80.0%
Other Punctuation 66074
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 64004
24.2%
0 44773
16.9%
9 44184
16.7%
2 22593
 
8.5%
7 20084
 
7.6%
6 18223
 
6.9%
8 16134
 
6.1%
5 13180
 
5.0%
3 11011
 
4.2%
4 10110
 
3.8%
Other Punctuation
ValueCountFrequency (%)
/ 66074
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 330370
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 66074
20.0%
1 64004
19.4%
0 44773
13.6%
9 44184
13.4%
2 22593
 
6.8%
7 20084
 
6.1%
6 18223
 
5.5%
8 16134
 
4.9%
5 13180
 
4.0%
3 11011
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 330370
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 66074
20.0%
1 64004
19.4%
0 44773
13.6%
9 44184
13.4%
2 22593
 
6.8%
7 20084
 
6.1%
6 18223
 
5.5%
8 16134
 
4.9%
5 13180
 
4.0%
3 11011
 
3.3%

NR_IDADE_DATA_POSSE
Real number (ℝ)

Distinct76
Distinct (%)0.2%
Missing62
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean47.57478
Minimum18
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.0 KiB
2022-12-06T12:48:51.393210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile29
Q140
median48
Q355
95-th percentile66
Maximum94
Range76
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.281107
Coefficient of variation (CV)0.23712367
Kurtosis-0.22943497
Mean47.57478
Median Absolute Deviation (MAD)8
Skewness0.065253207
Sum1571728
Variance127.26336
MonotonicityNot monotonic
2022-12-06T12:48:51.509159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 1175
 
3.5%
41 1109
 
3.4%
50 1100
 
3.3%
43 1088
 
3.3%
49 1068
 
3.2%
45 1068
 
3.2%
52 1066
 
3.2%
44 1063
 
3.2%
46 1055
 
3.2%
47 1030
 
3.1%
Other values (66) 22215
67.1%
ValueCountFrequency (%)
18 20
 
0.1%
19 38
 
0.1%
20 49
 
0.1%
21 105
0.3%
22 112
0.3%
23 123
0.4%
24 163
0.5%
25 197
0.6%
26 239
0.7%
27 236
0.7%
ValueCountFrequency (%)
94 1
 
< 0.1%
93 1
 
< 0.1%
91 1
 
< 0.1%
90 1
 
< 0.1%
89 1
 
< 0.1%
88 2
 
< 0.1%
87 2
 
< 0.1%
86 2
 
< 0.1%
85 8
< 0.1%
84 8
< 0.1%
Distinct30443
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.566933 × 1010
Minimum-4
Maximum8.8849744 × 1011
Zeros0
Zeros (%)0.0%
Negative62
Negative (%)0.2%
Memory size291.0 KiB
2022-12-06T12:48:51.631291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile1.7654775 × 1010
Q15.986548 × 1010
median8.685293 × 1010
Q31.1032434 × 1011
95-th percentile1.5048491 × 1011
Maximum8.8849744 × 1011
Range8.8849744 × 1011
Interquartile range (IQR)5.0458855 × 1010

Descriptive statistics

Standard deviation4.086353 × 1010
Coefficient of variation (CV)0.47699135
Kurtosis9.2748179
Mean8.566933 × 1010
Median Absolute Deviation (MAD)2.499498 × 1010
Skewness0.83354121
Sum2.8355692 × 1015
Variance1.6698281 × 1021
MonotonicityNot monotonic
2022-12-06T12:48:51.731640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4 62
 
0.2%
87559120396 5
 
< 0.1%
20173050396 4
 
< 0.1%
82531930353 4
 
< 0.1%
111977170370 4
 
< 0.1%
88942750396 4
 
< 0.1%
33911920345 4
 
< 0.1%
72185730329 4
 
< 0.1%
134279620302 4
 
< 0.1%
87959530345 4
 
< 0.1%
Other values (30433) 33000
99.7%
ValueCountFrequency (%)
-4 62
0.2%
4510302 1
 
< 0.1%
5451023 1
 
< 0.1%
8060302 1
 
< 0.1%
8292801 2
 
< 0.1%
10210396 1
 
< 0.1%
31530345 1
 
< 0.1%
38262011 1
 
< 0.1%
57280329 1
 
< 0.1%
67540370 1
 
< 0.1%
ValueCountFrequency (%)
888497440302 1
< 0.1%
776425605290 1
< 0.1%
416241690175 1
< 0.1%
409386430108 1
< 0.1%
399404620175 1
< 0.1%
398968300167 1
< 0.1%
392997060116 1
< 0.1%
392899690183 1
< 0.1%
384143640141 1
< 0.1%
378357470167 1
< 0.1%

CD_GENERO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2
22103 
4
10934 
-4
 
62

Length

Max length2
Median length1
Mean length1.0018732
Min length1

Characters and Unicode

Total characters33161
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row2
4th row4
5th row4

Common Values

ValueCountFrequency (%)
2 22103
66.8%
4 10934
33.0%
-4 62
 
0.2%

Length

2022-12-06T12:48:51.831897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:51.932211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 22103
66.8%
4 10996
33.2%

Most occurring characters

ValueCountFrequency (%)
2 22103
66.7%
4 10996
33.2%
- 62
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33099
99.8%
Dash Punctuation 62
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 22103
66.8%
4 10996
33.2%
Dash Punctuation
ValueCountFrequency (%)
- 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 22103
66.7%
4 10996
33.2%
- 62
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 22103
66.7%
4 10996
33.2%
- 62
 
0.2%

DS_GENERO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
MASCULINO
22103 
FEMININO
10934 
NÃO DIVULGÁVEL
 
62

Length

Max length14
Median length9
Mean length8.6790235
Min length8

Characters and Unicode

Total characters287267
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMASCULINO
2nd rowFEMININO
3rd rowMASCULINO
4th rowFEMININO
5th rowFEMININO

Common Values

ValueCountFrequency (%)
MASCULINO 22103
66.8%
FEMININO 10934
33.0%
NÃO DIVULGÁVEL 62
 
0.2%

Length

2022-12-06T12:48:52.016880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:52.117207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
masculino 22103
66.7%
feminino 10934
33.0%
não 62
 
0.2%
divulgável 62
 
0.2%

Most occurring characters

ValueCountFrequency (%)
I 44033
15.3%
N 44033
15.3%
O 33099
11.5%
M 33037
11.5%
L 22227
7.7%
U 22165
7.7%
C 22103
7.7%
S 22103
7.7%
A 22103
7.7%
E 10996
 
3.8%
Other values (7) 11368
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 287205
> 99.9%
Space Separator 62
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 44033
15.3%
N 44033
15.3%
O 33099
11.5%
M 33037
11.5%
L 22227
7.7%
U 22165
7.7%
C 22103
7.7%
S 22103
7.7%
A 22103
7.7%
E 10996
 
3.8%
Other values (6) 11306
 
3.9%
Space Separator
ValueCountFrequency (%)
62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 287205
> 99.9%
Common 62
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 44033
15.3%
N 44033
15.3%
O 33099
11.5%
M 33037
11.5%
L 22227
7.7%
U 22165
7.7%
C 22103
7.7%
S 22103
7.7%
A 22103
7.7%
E 10996
 
3.8%
Other values (6) 11306
 
3.9%
Common
ValueCountFrequency (%)
62
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 287143
> 99.9%
None 124
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 44033
15.3%
N 44033
15.3%
O 33099
11.5%
M 33037
11.5%
L 22227
7.7%
U 22165
7.7%
C 22103
7.7%
S 22103
7.7%
A 22103
7.7%
E 10996
 
3.8%
Other values (5) 11244
 
3.9%
None
ValueCountFrequency (%)
à 62
50.0%
Á 62
50.0%

CD_GRAU_INSTRUCAO
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9890631
Minimum-4
Maximum8
Zeros0
Zeros (%)0.0%
Negative62
Negative (%)0.2%
Memory size291.0 KiB
2022-12-06T12:48:52.178895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile3
Q15
median6
Q38
95-th percentile8
Maximum8
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7000606
Coefficient of variation (CV)0.28386086
Kurtosis1.3515541
Mean5.9890631
Median Absolute Deviation (MAD)1
Skewness-0.80692946
Sum198232
Variance2.8902061
MonotonicityNot monotonic
2022-12-06T12:48:52.264078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
6 13375
40.4%
8 8980
27.1%
4 3401
 
10.3%
3 3068
 
9.3%
7 2070
 
6.3%
5 1602
 
4.8%
2 541
 
1.6%
-4 62
 
0.2%
ValueCountFrequency (%)
-4 62
 
0.2%
2 541
 
1.6%
3 3068
 
9.3%
4 3401
 
10.3%
5 1602
 
4.8%
6 13375
40.4%
7 2070
 
6.3%
8 8980
27.1%
ValueCountFrequency (%)
8 8980
27.1%
7 2070
 
6.3%
6 13375
40.4%
5 1602
 
4.8%
4 3401
 
10.3%
3 3068
 
9.3%
2 541
 
1.6%
-4 62
 
0.2%
Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
ENSINO MÉDIO COMPLETO
13375 
SUPERIOR COMPLETO
8980 
ENSINO FUNDAMENTAL COMPLETO
3401 
ENSINO FUNDAMENTAL INCOMPLETO
3068 
SUPERIOR INCOMPLETO
2070 
Other values (3)
2205 

Length

Max length29
Median length27
Mean length21.084323
Min length12

Characters and Unicode

Total characters697870
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENSINO MÉDIO COMPLETO
2nd rowENSINO MÉDIO COMPLETO
3rd rowENSINO MÉDIO COMPLETO
4th rowSUPERIOR COMPLETO
5th rowSUPERIOR COMPLETO

Common Values

ValueCountFrequency (%)
ENSINO MÉDIO COMPLETO 13375
40.4%
SUPERIOR COMPLETO 8980
27.1%
ENSINO FUNDAMENTAL COMPLETO 3401
 
10.3%
ENSINO FUNDAMENTAL INCOMPLETO 3068
 
9.3%
SUPERIOR INCOMPLETO 2070
 
6.3%
ENSINO MÉDIO INCOMPLETO 1602
 
4.8%
LÊ E ESCREVE 541
 
1.6%
NÃO DIVULGÁVEL 62
 
0.2%

Length

2022-12-06T12:48:52.364385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:52.480272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
completo 25756
29.2%
ensino 21446
24.3%
médio 14977
17.0%
superior 11050
12.5%
incompleto 6740
 
7.6%
fundamental 6469
 
7.3%
541
 
0.6%
e 541
 
0.6%
escreve 541
 
0.6%
não 62
 
0.1%

Most occurring characters

ValueCountFrequency (%)
O 112527
16.1%
E 73687
10.6%
N 62632
9.0%
55086
7.9%
I 54275
7.8%
M 53942
7.7%
P 43546
 
6.2%
L 39630
 
5.7%
T 38965
 
5.6%
C 33037
 
4.7%
Other values (12) 130543
18.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 642784
92.1%
Space Separator 55086
 
7.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 112527
17.5%
E 73687
11.5%
N 62632
9.7%
I 54275
8.4%
M 53942
8.4%
P 43546
 
6.8%
L 39630
 
6.2%
T 38965
 
6.1%
C 33037
 
5.1%
S 33037
 
5.1%
Other values (11) 97506
15.2%
Space Separator
ValueCountFrequency (%)
55086
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 642784
92.1%
Common 55086
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 112527
17.5%
E 73687
11.5%
N 62632
9.7%
I 54275
8.4%
M 53942
8.4%
P 43546
 
6.8%
L 39630
 
6.2%
T 38965
 
6.1%
C 33037
 
5.1%
S 33037
 
5.1%
Other values (11) 97506
15.2%
Common
ValueCountFrequency (%)
55086
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 682228
97.8%
None 15642
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 112527
16.5%
E 73687
10.8%
N 62632
9.2%
55086
8.1%
I 54275
8.0%
M 53942
7.9%
P 43546
 
6.4%
L 39630
 
5.8%
T 38965
 
5.7%
C 33037
 
4.8%
Other values (8) 114901
16.8%
None
ValueCountFrequency (%)
É 14977
95.7%
Ê 541
 
3.5%
à 62
 
0.4%
Á 62
 
0.4%

CD_ESTADO_CIVIL
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9801202
Minimum-4
Maximum9
Zeros0
Zeros (%)0.0%
Negative62
Negative (%)0.2%
Memory size291.0 KiB
2022-12-06T12:48:52.596875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile1
Q11
median3
Q33
95-th percentile9
Maximum9
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2940053
Coefficient of variation (CV)0.76976939
Kurtosis2.1617029
Mean2.9801202
Median Absolute Deviation (MAD)0
Skewness1.5877164
Sum98639
Variance5.2624605
MonotonicityNot monotonic
2022-12-06T12:48:52.664749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 17480
52.8%
1 11277
34.1%
9 3288
 
9.9%
5 683
 
2.1%
7 309
 
0.9%
-4 62
 
0.2%
ValueCountFrequency (%)
-4 62
 
0.2%
1 11277
34.1%
3 17480
52.8%
5 683
 
2.1%
7 309
 
0.9%
9 3288
 
9.9%
ValueCountFrequency (%)
9 3288
 
9.9%
7 309
 
0.9%
5 683
 
2.1%
3 17480
52.8%
1 11277
34.1%
-4 62
 
0.2%

DS_ESTADO_CIVIL
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
CASADO(A)
17480 
SOLTEIRO(A)
11277 
DIVORCIADO(A)
3288 
VIÚVO(A)
 
683
SEPARADO(A) JUDICIALMENTE
 
309

Length

Max length25
Median length9
Mean length10.216865
Min length8

Characters and Unicode

Total characters338168
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCASADO(A)
2nd rowSOLTEIRO(A)
3rd rowCASADO(A)
4th rowCASADO(A)
5th rowSOLTEIRO(A)

Common Values

ValueCountFrequency (%)
CASADO(A) 17480
52.8%
SOLTEIRO(A) 11277
34.1%
DIVORCIADO(A) 3288
 
9.9%
VIÚVO(A) 683
 
2.1%
SEPARADO(A) JUDICIALMENTE 309
 
0.9%
NÃO DIVULGÁVEL 62
 
0.2%

Length

2022-12-06T12:48:52.780636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:52.951921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
casado(a 17480
52.2%
solteiro(a 11277
33.7%
divorciado(a 3288
 
9.8%
viúvo(a 683
 
2.0%
separado(a 309
 
0.9%
judicialmente 309
 
0.9%
não 62
 
0.2%
divulgável 62
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 72212
21.4%
O 47664
14.1%
( 33037
9.8%
) 33037
9.8%
S 29066
8.6%
D 24736
 
7.3%
C 21077
 
6.2%
I 19216
 
5.7%
R 14874
 
4.4%
E 12266
 
3.6%
Other values (13) 30983
9.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 271723
80.4%
Open Punctuation 33037
 
9.8%
Close Punctuation 33037
 
9.8%
Space Separator 371
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 72212
26.6%
O 47664
17.5%
S 29066
10.7%
D 24736
 
9.1%
C 21077
 
7.8%
I 19216
 
7.1%
R 14874
 
5.5%
E 12266
 
4.5%
L 11710
 
4.3%
T 11586
 
4.3%
Other values (10) 7316
 
2.7%
Open Punctuation
ValueCountFrequency (%)
( 33037
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33037
100.0%
Space Separator
ValueCountFrequency (%)
371
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 271723
80.4%
Common 66445
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 72212
26.6%
O 47664
17.5%
S 29066
10.7%
D 24736
 
9.1%
C 21077
 
7.8%
I 19216
 
7.1%
R 14874
 
5.5%
E 12266
 
4.5%
L 11710
 
4.3%
T 11586
 
4.3%
Other values (10) 7316
 
2.7%
Common
ValueCountFrequency (%)
( 33037
49.7%
) 33037
49.7%
371
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 337361
99.8%
None 807
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 72212
21.4%
O 47664
14.1%
( 33037
9.8%
) 33037
9.8%
S 29066
8.6%
D 24736
 
7.3%
C 21077
 
6.2%
I 19216
 
5.7%
R 14874
 
4.4%
E 12266
 
3.6%
Other values (10) 30176
8.9%
None
ValueCountFrequency (%)
Ú 683
84.6%
à 62
 
7.7%
Á 62
 
7.7%

CD_COR_RACA
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8782138
Minimum-4
Maximum6
Zeros0
Zeros (%)0.0%
Negative62
Negative (%)0.2%
Memory size291.0 KiB
2022-12-06T12:48:53.186964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile1
Q11
median1
Q33
95-th percentile3
Maximum6
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0892839
Coefficient of variation (CV)0.57995733
Kurtosis3.2596635
Mean1.8782138
Median Absolute Deviation (MAD)1
Skewness0.87771877
Sum62167
Variance1.1865394
MonotonicityNot monotonic
2022-12-06T12:48:53.382259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 16526
49.9%
3 10032
30.3%
2 5730
 
17.3%
6 646
 
2.0%
-4 62
 
0.2%
4 58
 
0.2%
5 45
 
0.1%
ValueCountFrequency (%)
-4 62
 
0.2%
1 16526
49.9%
2 5730
 
17.3%
3 10032
30.3%
4 58
 
0.2%
5 45
 
0.1%
6 646
 
2.0%
ValueCountFrequency (%)
6 646
 
2.0%
5 45
 
0.1%
4 58
 
0.2%
3 10032
30.3%
2 5730
 
17.3%
1 16526
49.9%
-4 62
 
0.2%

DS_COR_RACA
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
BRANCA
16526 
PARDA
10032 
PRETA
5730 
NÃO INFORMADO
 
646
NÃO DIVULGÁVEL
 
62
Other values (2)
 
103

Length

Max length14
Median length13
Mean length5.6798695
Min length5

Characters and Unicode

Total characters187998
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRANCA
2nd rowBRANCA
3rd rowBRANCA
4th rowBRANCA
5th rowPARDA

Common Values

ValueCountFrequency (%)
BRANCA 16526
49.9%
PARDA 10032
30.3%
PRETA 5730
 
17.3%
NÃO INFORMADO 646
 
2.0%
NÃO DIVULGÁVEL 62
 
0.2%
AMARELA 58
 
0.2%
INDÍGENA 45
 
0.1%

Length

2022-12-06T12:48:53.493634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:53.615317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
branca 16526
48.9%
parda 10032
29.7%
preta 5730
 
16.9%
não 708
 
2.1%
informado 646
 
1.9%
divulgável 62
 
0.2%
amarela 58
 
0.2%
indígena 45
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 59711
31.8%
R 32992
17.5%
N 17970
 
9.6%
B 16526
 
8.8%
C 16526
 
8.8%
P 15762
 
8.4%
D 10785
 
5.7%
E 5895
 
3.1%
T 5730
 
3.0%
O 2000
 
1.1%
Other values (11) 4101
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 187290
99.6%
Space Separator 708
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 59711
31.9%
R 32992
17.6%
N 17970
 
9.6%
B 16526
 
8.8%
C 16526
 
8.8%
P 15762
 
8.4%
D 10785
 
5.8%
E 5895
 
3.1%
T 5730
 
3.1%
O 2000
 
1.1%
Other values (10) 3393
 
1.8%
Space Separator
ValueCountFrequency (%)
708
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 187290
99.6%
Common 708
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 59711
31.9%
R 32992
17.6%
N 17970
 
9.6%
B 16526
 
8.8%
C 16526
 
8.8%
P 15762
 
8.4%
D 10785
 
5.8%
E 5895
 
3.1%
T 5730
 
3.1%
O 2000
 
1.1%
Other values (10) 3393
 
1.8%
Common
ValueCountFrequency (%)
708
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187183
99.6%
None 815
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 59711
31.9%
R 32992
17.6%
N 17970
 
9.6%
B 16526
 
8.8%
C 16526
 
8.8%
P 15762
 
8.4%
D 10785
 
5.8%
E 5895
 
3.1%
T 5730
 
3.1%
O 2000
 
1.1%
Other values (8) 3286
 
1.8%
None
ValueCountFrequency (%)
à 708
86.9%
Á 62
 
7.6%
Í 45
 
5.5%

CD_OCUPACAO
Real number (ℝ)

Distinct226
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean508.66543
Minimum-4
Maximum999
Zeros0
Zeros (%)0.0%
Negative62
Negative (%)0.2%
Memory size291.0 KiB
2022-12-06T12:48:53.759048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile125
Q1233
median298
Q3999
95-th percentile999
Maximum999
Range1003
Interquartile range (IQR)766

Descriptive statistics

Standard deviation346.97875
Coefficient of variation (CV)0.68213551
Kurtosis-1.5008881
Mean508.66543
Median Absolute Deviation (MAD)173
Skewness0.45103265
Sum16836317
Variance120394.25
MonotonicityNot monotonic
2022-12-06T12:48:53.890002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999 8417
25.4%
257 2284
 
6.9%
169 1954
 
5.9%
298 1346
 
4.1%
581 1243
 
3.8%
131 1184
 
3.6%
923 1060
 
3.2%
278 940
 
2.8%
233 576
 
1.7%
266 545
 
1.6%
Other values (216) 13550
40.9%
ValueCountFrequency (%)
-4 62
 
0.2%
101 224
0.7%
102 40
 
0.1%
103 5
 
< 0.1%
104 9
 
< 0.1%
106 1
 
< 0.1%
107 2
 
< 0.1%
109 126
0.4%
110 10
 
< 0.1%
111 312
0.9%
ValueCountFrequency (%)
999 8417
25.4%
931 434
 
1.3%
923 1060
 
3.2%
922 119
 
0.4%
921 273
 
0.8%
910 78
 
0.2%
907 1
 
< 0.1%
717 2
 
< 0.1%
715 2
 
< 0.1%
713 54
 
0.2%

DS_OCUPACAO
Categorical

Distinct226
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
OUTROS
8417 
EMPRESÁRIO
2284 
COMERCIANTE
1954 
SERVIDOR PÚBLICO MUNICIPAL
 
1346
DONA DE CASA
 
1243
Other values (221)
17855 

Length

Max length70
Median length61
Mean length17.26599
Min length6

Characters and Unicode

Total characters571487
Distinct characters40
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.1%

Sample

1st rowSERVIDOR PÚBLICO MUNICIPAL
2nd rowOUTROS
3rd rowPOLICIAL MILITAR
4th rowDONA DE CASA
5th rowOUTROS

Common Values

ValueCountFrequency (%)
OUTROS 8417
25.4%
EMPRESÁRIO 2284
 
6.9%
COMERCIANTE 1954
 
5.9%
SERVIDOR PÚBLICO MUNICIPAL 1346
 
4.1%
DONA DE CASA 1243
 
3.8%
ADVOGADO 1184
 
3.6%
APOSENTADO (EXCETO SERVIDOR PÚBLICO) 1060
 
3.2%
VEREADOR 940
 
2.8%
POLICIAL MILITAR 576
 
1.7%
PROFESSOR DE ENSINO MÉDIO 545
 
1.6%
Other values (216) 13550
40.9%

Length

2022-12-06T12:48:54.037148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
outros 8417
 
11.4%
de 6957
 
9.4%
e 4546
 
6.2%
servidor 3067
 
4.2%
público 3067
 
4.2%
empresário 2284
 
3.1%
comerciante 1954
 
2.7%
assemelhados 1734
 
2.4%
exceto 1388
 
1.9%
professor 1378
 
1.9%
Other values (369) 38907
52.8%

Most occurring characters

ValueCountFrequency (%)
O 67990
11.9%
E 60346
10.6%
R 51113
 
8.9%
A 44704
 
7.8%
I 41910
 
7.3%
S 41388
 
7.2%
40600
 
7.1%
T 35337
 
6.2%
D 27224
 
4.8%
C 23829
 
4.2%
Other values (30) 137046
24.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 524948
91.9%
Space Separator 40600
 
7.1%
Other Punctuation 2902
 
0.5%
Open Punctuation 1388
 
0.2%
Close Punctuation 1388
 
0.2%
Dash Punctuation 261
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 67990
13.0%
E 60346
11.5%
R 51113
9.7%
A 44704
8.5%
I 41910
 
8.0%
S 41388
 
7.9%
T 35337
 
6.7%
D 27224
 
5.2%
C 23829
 
4.5%
N 20417
 
3.9%
Other values (25) 110690
21.1%
Space Separator
ValueCountFrequency (%)
40600
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2902
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1388
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1388
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 524948
91.9%
Common 46539
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 67990
13.0%
E 60346
11.5%
R 51113
9.7%
A 44704
8.5%
I 41910
 
8.0%
S 41388
 
7.9%
T 35337
 
6.7%
D 27224
 
5.2%
C 23829
 
4.5%
N 20417
 
3.9%
Other values (25) 110690
21.1%
Common
ValueCountFrequency (%)
40600
87.2%
, 2902
 
6.2%
( 1388
 
3.0%
) 1388
 
3.0%
- 261
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 557644
97.6%
None 13843
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 67990
12.2%
E 60346
10.8%
R 51113
9.2%
A 44704
 
8.0%
I 41910
 
7.5%
S 41388
 
7.4%
40600
 
7.3%
T 35337
 
6.3%
D 27224
 
4.9%
C 23829
 
4.3%
Other values (19) 123203
22.1%
None
ValueCountFrequency (%)
Á 3649
26.4%
Ú 3471
25.1%
É 2183
15.8%
Í 1091
 
7.9%
Ó 1021
 
7.4%
Ç 987
 
7.1%
à 933
 
6.7%
 220
 
1.6%
Õ 131
 
0.9%
Ô 116
 
0.8%

VR_DESPESA_MAX_CAMPANHA
Unsupported

REJECTED
UNSUPPORTED

Missing14
Missing (%)< 0.1%
Memory size2.0 MiB

CD_SIT_TOT_TURNO
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing885
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean4.4623456
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.0 KiB
2022-12-06T12:48:54.137491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.71819995
Coefficient of variation (CV)0.16094674
Kurtosis4.4752467
Mean4.4623456
Median Absolute Deviation (MAD)0
Skewness-1.7570604
Sum143750
Variance0.51581117
MonotonicityNot monotonic
2022-12-06T12:48:54.206510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 17694
53.5%
4 12883
38.9%
2 841
 
2.5%
3 575
 
1.7%
1 197
 
0.6%
6 24
 
0.1%
(Missing) 885
 
2.7%
ValueCountFrequency (%)
1 197
 
0.6%
2 841
 
2.5%
3 575
 
1.7%
4 12883
38.9%
5 17694
53.5%
6 24
 
0.1%
ValueCountFrequency (%)
6 24
 
0.1%
5 17694
53.5%
4 12883
38.9%
3 575
 
1.7%
2 841
 
2.5%
1 197
 
0.6%

DS_SIT_TOT_TURNO
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing885
Missing (%)2.7%
Memory size2.4 MiB
SUPLENTE
17694 
NÃO ELEITO
12883 
ELEITO POR QP
 
841
ELEITO POR MÉDIA
 
575
ELEITO
 
197

Length

Max length16
Median length8
Mean length9.0609362
Min length6

Characters and Unicode

Total characters291889
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUPLENTE
2nd rowSUPLENTE
3rd rowSUPLENTE
4th rowSUPLENTE
5th rowSUPLENTE

Common Values

ValueCountFrequency (%)
SUPLENTE 17694
53.5%
NÃO ELEITO 12883
38.9%
ELEITO POR QP 841
 
2.5%
ELEITO POR MÉDIA 575
 
1.7%
ELEITO 197
 
0.6%
2º TURNO 24
 
0.1%
(Missing) 885
 
2.7%

Length

2022-12-06T12:48:54.301160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:54.406994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
suplente 17694
36.9%
eleito 14496
30.2%
não 12883
26.9%
por 1416
 
3.0%
qp 841
 
1.8%
média 575
 
1.2%
24
 
0.1%
turno 24
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 64380
22.1%
T 32214
11.0%
L 32190
11.0%
N 30601
10.5%
O 28819
9.9%
P 19951
 
6.8%
U 17718
 
6.1%
S 17694
 
6.1%
15739
 
5.4%
I 15071
 
5.2%
Other values (9) 17512
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 276102
94.6%
Space Separator 15739
 
5.4%
Decimal Number 24
 
< 0.1%
Other Letter 24
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 64380
23.3%
T 32214
11.7%
L 32190
11.7%
N 30601
11.1%
O 28819
10.4%
P 19951
 
7.2%
U 17718
 
6.4%
S 17694
 
6.4%
I 15071
 
5.5%
à 12883
 
4.7%
Other values (6) 4581
 
1.7%
Space Separator
ValueCountFrequency (%)
15739
100.0%
Decimal Number
ValueCountFrequency (%)
2 24
100.0%
Other Letter
ValueCountFrequency (%)
º 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 276126
94.6%
Common 15763
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 64380
23.3%
T 32214
11.7%
L 32190
11.7%
N 30601
11.1%
O 28819
10.4%
P 19951
 
7.2%
U 17718
 
6.4%
S 17694
 
6.4%
I 15071
 
5.5%
à 12883
 
4.7%
Other values (7) 4605
 
1.7%
Common
ValueCountFrequency (%)
15739
99.8%
2 24
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278407
95.4%
None 13482
 
4.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 64380
23.1%
T 32214
11.6%
L 32190
11.6%
N 30601
11.0%
O 28819
10.4%
P 19951
 
7.2%
U 17718
 
6.4%
S 17694
 
6.4%
15739
 
5.7%
I 15071
 
5.4%
Other values (6) 4030
 
1.4%
None
ValueCountFrequency (%)
à 12883
95.6%
É 575
 
4.3%
º 24
 
0.2%

ST_REELEICAO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
N
32314 
S
 
723
Não divulgável
 
62

Length

Max length14
Median length1
Mean length1.0243512
Min length1

Characters and Unicode

Total characters33905
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 32314
97.6%
S 723
 
2.2%
Não divulgável 62
 
0.2%

Length

2022-12-06T12:48:54.507314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:54.610771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
n 32314
97.4%
s 723
 
2.2%
não 62
 
0.2%
divulgável 62
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 32376
95.5%
S 723
 
2.1%
v 124
 
0.4%
l 124
 
0.4%
ã 62
 
0.2%
o 62
 
0.2%
62
 
0.2%
d 62
 
0.2%
i 62
 
0.2%
u 62
 
0.2%
Other values (3) 186
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 33099
97.6%
Lowercase Letter 744
 
2.2%
Space Separator 62
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 124
16.7%
l 124
16.7%
ã 62
8.3%
o 62
8.3%
d 62
8.3%
i 62
8.3%
u 62
8.3%
g 62
8.3%
á 62
8.3%
e 62
8.3%
Uppercase Letter
ValueCountFrequency (%)
N 32376
97.8%
S 723
 
2.2%
Space Separator
ValueCountFrequency (%)
62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33843
99.8%
Common 62
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 32376
95.7%
S 723
 
2.1%
v 124
 
0.4%
l 124
 
0.4%
ã 62
 
0.2%
o 62
 
0.2%
d 62
 
0.2%
i 62
 
0.2%
u 62
 
0.2%
g 62
 
0.2%
Other values (2) 124
 
0.4%
Common
ValueCountFrequency (%)
62
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33781
99.6%
None 124
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 32376
95.8%
S 723
 
2.1%
v 124
 
0.4%
l 124
 
0.4%
o 62
 
0.2%
62
 
0.2%
d 62
 
0.2%
i 62
 
0.2%
u 62
 
0.2%
g 62
 
0.2%
None
ValueCountFrequency (%)
ã 62
50.0%
á 62
50.0%

ST_DECLARAR_BENS
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
S
25851 
N
7186 
Não divulgável
 
62

Length

Max length14
Median length1
Mean length1.0243512
Min length1

Characters and Unicode

Total characters33905
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowN
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 25851
78.1%
N 7186
 
21.7%
Não divulgável 62
 
0.2%

Length

2022-12-06T12:48:54.692014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:54.776644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
s 25851
78.0%
n 7186
 
21.7%
não 62
 
0.2%
divulgável 62
 
0.2%

Most occurring characters

ValueCountFrequency (%)
S 25851
76.2%
N 7248
 
21.4%
v 124
 
0.4%
l 124
 
0.4%
ã 62
 
0.2%
o 62
 
0.2%
62
 
0.2%
d 62
 
0.2%
i 62
 
0.2%
u 62
 
0.2%
Other values (3) 186
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 33099
97.6%
Lowercase Letter 744
 
2.2%
Space Separator 62
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 124
16.7%
l 124
16.7%
ã 62
8.3%
o 62
8.3%
d 62
8.3%
i 62
8.3%
u 62
8.3%
g 62
8.3%
á 62
8.3%
e 62
8.3%
Uppercase Letter
ValueCountFrequency (%)
S 25851
78.1%
N 7248
 
21.9%
Space Separator
ValueCountFrequency (%)
62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33843
99.8%
Common 62
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 25851
76.4%
N 7248
 
21.4%
v 124
 
0.4%
l 124
 
0.4%
ã 62
 
0.2%
o 62
 
0.2%
d 62
 
0.2%
i 62
 
0.2%
u 62
 
0.2%
g 62
 
0.2%
Other values (2) 124
 
0.4%
Common
ValueCountFrequency (%)
62
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33781
99.6%
None 124
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 25851
76.5%
N 7248
 
21.5%
v 124
 
0.4%
l 124
 
0.4%
o 62
 
0.2%
62
 
0.2%
d 62
 
0.2%
i 62
 
0.2%
u 62
 
0.2%
g 62
 
0.2%
None
ValueCountFrequency (%)
ã 62
50.0%
á 62
50.0%

NR_PROTOCOLO_CANDIDATURA
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing33099
Missing (%)100.0%
Memory size291.0 KiB

NR_PROCESSO
Real number (ℝ)

Distinct33075
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2982942 × 1014
Minimum6.0001077 × 1018
Maximum6.0745248 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.0 KiB
2022-12-06T12:48:54.876874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.0001077 × 1018
5-th percentile6.001135 × 1018
Q16.0023323 × 1018
median6.0039836 × 1018
Q36.0079635 × 1018
95-th percentile6.0310056 × 1018
Maximum6.0745248 × 1018
Range7.44171 × 1016
Interquartile range (IQR)5.6312 × 1015

Descriptive statistics

Standard deviation9.346051 × 1015
Coefficient of variation (CV)-71.987158
Kurtosis4.7083452
Mean-1.2982942 × 1014
Median Absolute Deviation (MAD)2.0823 × 1015
Skewness2.1843823
Sum-4.297224 × 1018
Variance8.734867 × 1031
MonotonicityNot monotonic
2022-12-06T12:48:55.009631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6001952820206190592 2
 
< 0.1%
6005451620206190592 2
 
< 0.1%
6004841420206190592 2
 
< 0.1%
6004385220206189568 2
 
< 0.1%
6004393720206189568 2
 
< 0.1%
6018195620186189824 2
 
< 0.1%
6006269820206189568 2
 
< 0.1%
6001961320206190592 2
 
< 0.1%
6003798220206189568 2
 
< 0.1%
6018160420186189824 2
 
< 0.1%
Other values (33065) 33079
99.9%
ValueCountFrequency (%)
6000107720226190336 1
< 0.1%
6000116220226190336 1
< 0.1%
6000124720226190336 1
< 0.1%
6000133220226190336 1
< 0.1%
6000141720226190336 1
< 0.1%
6000150220226190336 1
< 0.1%
6000169520206189568 1
< 0.1%
6000178020206189568 1
< 0.1%
6000186520206189568 1
< 0.1%
6000195020206189568 1
< 0.1%
ValueCountFrequency (%)
6074524820186189824 1
< 0.1%
6074499320186189824 1
< 0.1%
6074472620186189824 1
< 0.1%
6074455620186189824 1
< 0.1%
6074394920186189824 1
< 0.1%
6073684720186189824 1
< 0.1%
6073667720186189824 1
< 0.1%
6073659220186189824 1
< 0.1%
6073641020186189824 1
< 0.1%
6073632520186189824 1
< 0.1%

CD_SITUACAO_CANDIDATO_PLEITO
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)< 0.1%
Missing885
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean2.4054448
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.0 KiB
2022-12-06T12:48:55.120209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile4
Maximum20
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0460465
Coefficient of variation (CV)0.85058966
Kurtosis28.49697
Mean2.4054448
Median Absolute Deviation (MAD)0
Skewness5.4374427
Sum77489
Variance4.1863063
MonotonicityNot monotonic
2022-12-06T12:48:55.202911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 30466
92.0%
14 816
 
2.5%
4 675
 
2.0%
6 159
 
0.5%
16 56
 
0.2%
17 17
 
0.1%
13 17
 
0.1%
8 4
 
< 0.1%
7 3
 
< 0.1%
20 1
 
< 0.1%
(Missing) 885
 
2.7%
ValueCountFrequency (%)
2 30466
92.0%
4 675
 
2.0%
6 159
 
0.5%
7 3
 
< 0.1%
8 4
 
< 0.1%
13 17
 
0.1%
14 816
 
2.5%
16 56
 
0.2%
17 17
 
0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
17 17
 
0.1%
16 56
 
0.2%
14 816
 
2.5%
13 17
 
0.1%
8 4
 
< 0.1%
7 3
 
< 0.1%
6 159
 
0.5%
4 675
 
2.0%
2 30466
92.0%

DS_SITUACAO_CANDIDATO_PLEITO
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)< 0.1%
Missing885
Missing (%)2.7%
Memory size2.0 MiB
DEFERIDO
30466 
INDEFERIDO
 
816
INDEFERIDO COM RECURSO
 
675
RENÚNCIA
 
159
DEFERIDO COM RECURSO
 
56
Other values (5)
 
42

Length

Max length32
Median length8
Mean length8.3812318
Min length8

Characters and Unicode

Total characters269993
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDEFERIDO
2nd rowDEFERIDO
3rd rowDEFERIDO
4th rowDEFERIDO
5th rowDEFERIDO

Common Values

ValueCountFrequency (%)
DEFERIDO 30466
92.0%
INDEFERIDO 816
 
2.5%
INDEFERIDO COM RECURSO 675
 
2.0%
RENÚNCIA 159
 
0.5%
DEFERIDO COM RECURSO 56
 
0.2%
PENDENTE DE JULGAMENTO 17
 
0.1%
PEDIDO NÃO CONHECIDO 17
 
0.1%
AGUARDANDO JULGAMENTO 4
 
< 0.1%
FALECIMENTO 3
 
< 0.1%
PEDIDO NÃO CONHECIDO COM RECURSO 1
 
< 0.1%
(Missing) 885
 
2.7%

Length

2022-12-06T12:48:55.295512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:55.409075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
deferido 30522
90.4%
indeferido 1491
 
4.4%
com 732
 
2.2%
recurso 732
 
2.2%
renúncia 159
 
0.5%
julgamento 21
 
0.1%
pedido 18
 
0.1%
não 18
 
0.1%
conhecido 18
 
0.1%
pendente 17
 
0.1%
Other values (3) 24
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 65048
24.1%
D 64122
23.7%
I 33702
12.5%
R 33640
12.5%
O 33577
12.4%
F 32016
11.9%
N 1907
 
0.7%
C 1662
 
0.6%
1538
 
0.6%
U 757
 
0.3%
Other values (11) 2024
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 268455
99.4%
Space Separator 1538
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 65048
24.2%
D 64122
23.9%
I 33702
12.6%
R 33640
12.5%
O 33577
12.5%
F 32016
11.9%
N 1907
 
0.7%
C 1662
 
0.6%
U 757
 
0.3%
M 756
 
0.3%
Other values (10) 1268
 
0.5%
Space Separator
ValueCountFrequency (%)
1538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 268455
99.4%
Common 1538
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 65048
24.2%
D 64122
23.9%
I 33702
12.6%
R 33640
12.5%
O 33577
12.5%
F 32016
11.9%
N 1907
 
0.7%
C 1662
 
0.6%
U 757
 
0.3%
M 756
 
0.3%
Other values (10) 1268
 
0.5%
Common
ValueCountFrequency (%)
1538
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269816
99.9%
None 177
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 65048
24.1%
D 64122
23.8%
I 33702
12.5%
R 33640
12.5%
O 33577
12.4%
F 32016
11.9%
N 1907
 
0.7%
C 1662
 
0.6%
1538
 
0.6%
U 757
 
0.3%
Other values (9) 1847
 
0.7%
None
ValueCountFrequency (%)
Ú 159
89.8%
à 18
 
10.2%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2
28859 
4
 
2020
17
 
1260
<NA>
 
885
16
 
72

Length

Max length4
Median length1
Mean length1.1205474
Min length1

Characters and Unicode

Total characters37089
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 28859
87.2%
4 2020
 
6.1%
17 1260
 
3.8%
<NA> 885
 
2.7%
16 72
 
0.2%
20 3
 
< 0.1%

Length

2022-12-06T12:48:55.529331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:55.633779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 28859
87.2%
4 2020
 
6.1%
17 1260
 
3.8%
na 885
 
2.7%
16 72
 
0.2%
20 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 28862
77.8%
4 2020
 
5.4%
1 1332
 
3.6%
7 1260
 
3.4%
< 885
 
2.4%
N 885
 
2.4%
A 885
 
2.4%
> 885
 
2.4%
6 72
 
0.2%
0 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33549
90.5%
Math Symbol 1770
 
4.8%
Uppercase Letter 1770
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 28862
86.0%
4 2020
 
6.0%
1 1332
 
4.0%
7 1260
 
3.8%
6 72
 
0.2%
0 3
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
< 885
50.0%
> 885
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 885
50.0%
A 885
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35319
95.2%
Latin 1770
 
4.8%

Most frequent character per script

Common
ValueCountFrequency (%)
2 28862
81.7%
4 2020
 
5.7%
1 1332
 
3.8%
7 1260
 
3.6%
< 885
 
2.5%
> 885
 
2.5%
6 72
 
0.2%
0 3
 
< 0.1%
Latin
ValueCountFrequency (%)
N 885
50.0%
A 885
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 28862
77.8%
4 2020
 
5.4%
1 1332
 
3.6%
7 1260
 
3.4%
< 885
 
2.4%
N 885
 
2.4%
A 885
 
2.4%
> 885
 
2.4%
6 72
 
0.2%
0 3
 
< 0.1%

DS_SITUACAO_CANDIDATO_URNA
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing885
Missing (%)2.7%
Memory size2.1 MiB
DEFERIDO
28859 
INDEFERIDO COM RECURSO
 
2020
PENDENTE DE JULGAMENTO
 
1260
DEFERIDO COM RECURSO
 
72
PEDIDO NÃO CONHECIDO COM RECURSO
 
3

Length

Max length32
Median length8
Mean length9.4545229
Min length8

Characters and Unicode

Total characters304568
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEFERIDO
2nd rowDEFERIDO
3rd rowDEFERIDO
4th rowDEFERIDO
5th rowDEFERIDO

Common Values

ValueCountFrequency (%)
DEFERIDO 28859
87.2%
INDEFERIDO COM RECURSO 2020
 
6.1%
PENDENTE DE JULGAMENTO 1260
 
3.8%
DEFERIDO COM RECURSO 72
 
0.2%
PEDIDO NÃO CONHECIDO COM RECURSO 3
 
< 0.1%
(Missing) 885
 
2.7%

Length

2022-12-06T12:48:55.715392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:55.817660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
deferido 28931
74.3%
com 2095
 
5.4%
recurso 2095
 
5.4%
indeferido 2020
 
5.2%
pendente 1260
 
3.2%
de 1260
 
3.2%
julgamento 1260
 
3.2%
pedido 3
 
< 0.1%
não 3
 
< 0.1%
conhecido 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 70303
23.1%
D 64431
21.2%
O 36413
12.0%
R 35141
11.5%
I 32977
10.8%
F 30951
10.2%
6716
 
2.2%
N 5806
 
1.9%
C 4196
 
1.4%
U 3355
 
1.1%
Other values (10) 14279
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 297852
97.8%
Space Separator 6716
 
2.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 70303
23.6%
D 64431
21.6%
O 36413
12.2%
R 35141
11.8%
I 32977
11.1%
F 30951
10.4%
N 5806
 
1.9%
C 4196
 
1.4%
U 3355
 
1.1%
M 3355
 
1.1%
Other values (9) 10924
 
3.7%
Space Separator
ValueCountFrequency (%)
6716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 297852
97.8%
Common 6716
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 70303
23.6%
D 64431
21.6%
O 36413
12.2%
R 35141
11.8%
I 32977
11.1%
F 30951
10.4%
N 5806
 
1.9%
C 4196
 
1.4%
U 3355
 
1.1%
M 3355
 
1.1%
Other values (9) 10924
 
3.7%
Common
ValueCountFrequency (%)
6716
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 304565
> 99.9%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 70303
23.1%
D 64431
21.2%
O 36413
12.0%
R 35141
11.5%
I 32977
10.8%
F 30951
10.2%
6716
 
2.2%
N 5806
 
1.9%
C 4196
 
1.4%
U 3355
 
1.1%
Other values (9) 14276
 
4.7%
None
ValueCountFrequency (%)
à 3
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
SIM
32230 
NÃO
 
869

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters99297
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSIM
2nd rowSIM
3rd rowSIM
4th rowSIM
5th rowSIM

Common Values

ValueCountFrequency (%)
SIM 32230
97.4%
NÃO 869
 
2.6%

Length

2022-12-06T12:48:55.911057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:56.004791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
sim 32230
97.4%
não 869
 
2.6%

Most occurring characters

ValueCountFrequency (%)
S 32230
32.5%
I 32230
32.5%
M 32230
32.5%
N 869
 
0.9%
à 869
 
0.9%
O 869
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 99297
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 32230
32.5%
I 32230
32.5%
M 32230
32.5%
N 869
 
0.9%
à 869
 
0.9%
O 869
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 99297
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 32230
32.5%
I 32230
32.5%
M 32230
32.5%
N 869
 
0.9%
à 869
 
0.9%
O 869
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98428
99.1%
None 869
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 32230
32.7%
I 32230
32.7%
M 32230
32.7%
N 869
 
0.9%
O 869
 
0.9%
None
ValueCountFrequency (%)
à 869
100.0%

NR_FEDERACAO
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
<NA>
32764 
1
 
122
2
 
122
3
 
91

Length

Max length4
Median length4
Mean length3.9696365
Min length1

Characters and Unicode

Total characters131391
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 32764
99.0%
1 122
 
0.4%
2 122
 
0.4%
3 91
 
0.3%

Length

2022-12-06T12:48:56.078475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:56.181385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
na 32764
99.0%
1 122
 
0.4%
2 122
 
0.4%
3 91
 
0.3%

Most occurring characters

ValueCountFrequency (%)
< 32764
24.9%
N 32764
24.9%
A 32764
24.9%
> 32764
24.9%
1 122
 
0.1%
2 122
 
0.1%
3 91
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Math Symbol 65528
49.9%
Uppercase Letter 65528
49.9%
Decimal Number 335
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 122
36.4%
2 122
36.4%
3 91
27.2%
Math Symbol
ValueCountFrequency (%)
< 32764
50.0%
> 32764
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 32764
50.0%
A 32764
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65863
50.1%
Latin 65528
49.9%

Most frequent character per script

Common
ValueCountFrequency (%)
< 32764
49.7%
> 32764
49.7%
1 122
 
0.2%
2 122
 
0.2%
3 91
 
0.1%
Latin
ValueCountFrequency (%)
N 32764
50.0%
A 32764
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 131391
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
< 32764
24.9%
N 32764
24.9%
A 32764
24.9%
> 32764
24.9%
1 122
 
0.1%
2 122
 
0.1%
3 91
 
0.1%

NM_FEDERACAO
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.9%
Missing32764
Missing (%)99.0%
Memory size1.3 MiB
Federação PSDB Cidadania
122 
Federação Brasil da Esperança - FE BRASIL
122 
Federação PSOL REDE
91 

Length

Max length41
Median length24
Mean length28.832836
Min length19

Characters and Unicode

Total characters9659
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFederação PSDB Cidadania
2nd rowFederação Brasil da Esperança - FE BRASIL
3rd rowFederação PSDB Cidadania
4th rowFederação PSDB Cidadania
5th rowFederação PSOL REDE

Common Values

ValueCountFrequency (%)
Federação PSDB Cidadania 122
 
0.4%
Federação Brasil da Esperança - FE BRASIL 122
 
0.4%
Federação PSOL REDE 91
 
0.3%
(Missing) 32764
99.0%

Length

2022-12-06T12:48:56.263052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:56.354636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
federação 335
22.4%
brasil 244
16.3%
psdb 122
 
8.2%
cidadania 122
 
8.2%
da 122
 
8.2%
esperança 122
 
8.2%
122
 
8.2%
fe 122
 
8.2%
psol 91
 
6.1%
rede 91
 
6.1%

Most occurring characters

ValueCountFrequency (%)
a 1189
 
12.3%
1158
 
12.0%
e 792
 
8.2%
d 701
 
7.3%
r 579
 
6.0%
F 457
 
4.7%
ç 457
 
4.7%
E 426
 
4.4%
B 366
 
3.8%
i 366
 
3.8%
Other values (16) 3168
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5486
56.8%
Uppercase Letter 2893
30.0%
Space Separator 1158
 
12.0%
Dash Punctuation 122
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1189
21.7%
e 792
14.4%
d 701
12.8%
r 579
10.6%
ç 457
 
8.3%
i 366
 
6.7%
o 335
 
6.1%
ã 335
 
6.1%
n 244
 
4.4%
s 244
 
4.4%
Other values (2) 244
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
F 457
15.8%
E 426
14.7%
B 366
12.7%
S 335
11.6%
D 213
7.4%
R 213
7.4%
P 213
7.4%
L 213
7.4%
I 122
 
4.2%
A 122
 
4.2%
Other values (2) 213
7.4%
Space Separator
ValueCountFrequency (%)
1158
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8379
86.7%
Common 1280
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1189
14.2%
e 792
 
9.5%
d 701
 
8.4%
r 579
 
6.9%
F 457
 
5.5%
ç 457
 
5.5%
E 426
 
5.1%
B 366
 
4.4%
i 366
 
4.4%
S 335
 
4.0%
Other values (14) 2711
32.4%
Common
ValueCountFrequency (%)
1158
90.5%
- 122
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8867
91.8%
None 792
 
8.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1189
13.4%
1158
13.1%
e 792
 
8.9%
d 701
 
7.9%
r 579
 
6.5%
F 457
 
5.2%
E 426
 
4.8%
B 366
 
4.1%
i 366
 
4.1%
S 335
 
3.8%
Other values (14) 2498
28.2%
None
ValueCountFrequency (%)
ç 457
57.7%
ã 335
42.3%

SG_FEDERACAO
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.9%
Missing32764
Missing (%)99.0%
Memory size1.3 MiB
PSDB/CIDADANIA
122 
PT/PC do B/PV
122 
PSOL/REDE
91 

Length

Max length14
Median length13
Mean length12.277612
Min length9

Characters and Unicode

Total characters4113
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPSDB/CIDADANIA
2nd rowPT/PC do B/PV
3rd rowPSDB/CIDADANIA
4th rowPSDB/CIDADANIA
5th rowPSOL/REDE

Common Values

ValueCountFrequency (%)
PSDB/CIDADANIA 122
 
0.4%
PT/PC do B/PV 122
 
0.4%
PSOL/REDE 91
 
0.3%
(Missing) 32764
99.0%

Length

2022-12-06T12:48:56.446660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:56.546065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
psdb/cidadania 122
21.1%
pt/pc 122
21.1%
do 122
21.1%
b/pv 122
21.1%
psol/rede 91
15.7%

Most occurring characters

ValueCountFrequency (%)
P 579
14.1%
D 457
11.1%
/ 457
11.1%
A 366
8.9%
B 244
 
5.9%
C 244
 
5.9%
I 244
 
5.9%
244
 
5.9%
S 213
 
5.2%
E 182
 
4.4%
Other values (8) 883
21.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3168
77.0%
Other Punctuation 457
 
11.1%
Space Separator 244
 
5.9%
Lowercase Letter 244
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 579
18.3%
D 457
14.4%
A 366
11.6%
B 244
7.7%
C 244
7.7%
I 244
7.7%
S 213
 
6.7%
E 182
 
5.7%
N 122
 
3.9%
V 122
 
3.9%
Other values (4) 395
12.5%
Lowercase Letter
ValueCountFrequency (%)
d 122
50.0%
o 122
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 457
100.0%
Space Separator
ValueCountFrequency (%)
244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3412
83.0%
Common 701
 
17.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 579
17.0%
D 457
13.4%
A 366
10.7%
B 244
 
7.2%
C 244
 
7.2%
I 244
 
7.2%
S 213
 
6.2%
E 182
 
5.3%
N 122
 
3.6%
d 122
 
3.6%
Other values (6) 639
18.7%
Common
ValueCountFrequency (%)
/ 457
65.2%
244
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 579
14.1%
D 457
11.1%
/ 457
11.1%
A 366
8.9%
B 244
 
5.9%
C 244
 
5.9%
I 244
 
5.9%
244
 
5.9%
S 213
 
5.2%
E 182
 
4.4%
Other values (8) 883
21.5%

DS_COMPOSICAO_FEDERACAO
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.9%
Missing32764
Missing (%)99.0%
Memory size1.3 MiB
PSDB/CIDADANIA
122 
PT/PC do B/PV
122 
PSOL/REDE
91 

Length

Max length14
Median length13
Mean length12.277612
Min length9

Characters and Unicode

Total characters4113
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPSDB/CIDADANIA
2nd rowPT/PC do B/PV
3rd rowPSDB/CIDADANIA
4th rowPSDB/CIDADANIA
5th rowPSOL/REDE

Common Values

ValueCountFrequency (%)
PSDB/CIDADANIA 122
 
0.4%
PT/PC do B/PV 122
 
0.4%
PSOL/REDE 91
 
0.3%
(Missing) 32764
99.0%

Length

2022-12-06T12:48:56.628219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:56.721884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
psdb/cidadania 122
21.1%
pt/pc 122
21.1%
do 122
21.1%
b/pv 122
21.1%
psol/rede 91
15.7%

Most occurring characters

ValueCountFrequency (%)
P 579
14.1%
D 457
11.1%
/ 457
11.1%
A 366
8.9%
B 244
 
5.9%
C 244
 
5.9%
I 244
 
5.9%
244
 
5.9%
S 213
 
5.2%
E 182
 
4.4%
Other values (8) 883
21.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3168
77.0%
Other Punctuation 457
 
11.1%
Space Separator 244
 
5.9%
Lowercase Letter 244
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 579
18.3%
D 457
14.4%
A 366
11.6%
B 244
7.7%
C 244
7.7%
I 244
7.7%
S 213
 
6.7%
E 182
 
5.7%
N 122
 
3.9%
V 122
 
3.9%
Other values (4) 395
12.5%
Lowercase Letter
ValueCountFrequency (%)
d 122
50.0%
o 122
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 457
100.0%
Space Separator
ValueCountFrequency (%)
244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3412
83.0%
Common 701
 
17.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 579
17.0%
D 457
13.4%
A 366
10.7%
B 244
 
7.2%
C 244
 
7.2%
I 244
 
7.2%
S 213
 
6.2%
E 182
 
5.3%
N 122
 
3.6%
d 122
 
3.6%
Other values (6) 639
18.7%
Common
ValueCountFrequency (%)
/ 457
65.2%
244
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 579
14.1%
D 457
11.1%
/ 457
11.1%
A 366
8.9%
B 244
 
5.9%
C 244
 
5.9%
I 244
 
5.9%
244
 
5.9%
S 213
 
5.2%
E 182
 
4.4%
Other values (8) 883
21.5%

NM_TIPO_DESTINACAO_VOTOS
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing5072
Missing (%)15.3%
Memory size2.5 MiB
Válido
27890 
Anulado sub judice
 
86
Nulo técnico
 
51

Length

Max length18
Median length6
Mean length6.0477397
Min length6

Characters and Unicode

Total characters169500
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVálido
2nd rowVálido
3rd rowVálido
4th rowVálido
5th rowVálido

Common Values

ValueCountFrequency (%)
Válido 27890
84.3%
Anulado sub judice 86
 
0.3%
Nulo técnico 51
 
0.2%
(Missing) 5072
 
15.3%

Length

2022-12-06T12:48:56.804298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:56.904499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
válido 27890
98.7%
anulado 86
 
0.3%
sub 86
 
0.3%
judice 86
 
0.3%
nulo 51
 
0.2%
técnico 51
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 28078
16.6%
d 28062
16.6%
l 28027
16.5%
i 28027
16.5%
V 27890
16.5%
á 27890
16.5%
u 309
 
0.2%
223
 
0.1%
c 188
 
0.1%
n 137
 
0.1%
Other values (9) 669
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 141250
83.3%
Uppercase Letter 28027
 
16.5%
Space Separator 223
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 28078
19.9%
d 28062
19.9%
l 28027
19.8%
i 28027
19.8%
á 27890
19.7%
u 309
 
0.2%
c 188
 
0.1%
n 137
 
0.1%
j 86
 
0.1%
e 86
 
0.1%
Other values (5) 360
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
V 27890
99.5%
A 86
 
0.3%
N 51
 
0.2%
Space Separator
ValueCountFrequency (%)
223
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 169277
99.9%
Common 223
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 28078
16.6%
d 28062
16.6%
l 28027
16.6%
i 28027
16.6%
V 27890
16.5%
á 27890
16.5%
u 309
 
0.2%
c 188
 
0.1%
n 137
 
0.1%
j 86
 
0.1%
Other values (8) 583
 
0.3%
Common
ValueCountFrequency (%)
223
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141559
83.5%
None 27941
 
16.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 28078
19.8%
d 28062
19.8%
l 28027
19.8%
i 28027
19.8%
V 27890
19.7%
u 309
 
0.2%
223
 
0.2%
c 188
 
0.1%
n 137
 
0.1%
j 86
 
0.1%
Other values (7) 532
 
0.4%
None
ValueCountFrequency (%)
á 27890
99.8%
é 51
 
0.2%

CD_SITUACAO_CANDIDATO_TOT
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)< 0.1%
Missing4437
Missing (%)13.4%
Infinite0
Infinite (%)0.0%
Mean10.699916
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.0 KiB
2022-12-06T12:48:56.968249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q112
median12
Q312
95-th percentile12
Maximum20
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.3046577
Coefficient of variation (CV)0.30884893
Kurtosis2.6331609
Mean10.699916
Median Absolute Deviation (MAD)0
Skewness-2.1427503
Sum306681
Variance10.920763
MonotonicityNot monotonic
2022-12-06T12:48:57.050198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
12 24735
74.7%
2 2571
 
7.8%
3 1209
 
3.7%
4 92
 
0.3%
14 45
 
0.1%
6 7
 
< 0.1%
16 2
 
< 0.1%
20 1
 
< 0.1%
(Missing) 4437
 
13.4%
ValueCountFrequency (%)
2 2571
 
7.8%
3 1209
 
3.7%
4 92
 
0.3%
6 7
 
< 0.1%
12 24735
74.7%
14 45
 
0.1%
16 2
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
16 2
 
< 0.1%
14 45
 
0.1%
12 24735
74.7%
6 7
 
< 0.1%
4 92
 
0.3%
3 1209
 
3.7%
2 2571
 
7.8%

DS_SITUACAO_CANDIDATO_TOT
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)< 0.1%
Missing4437
Missing (%)13.4%
Memory size1.9 MiB
Apto
24735 
Deferido
2571 
Inapto
 
1209
Indeferido com recurso
 
92
Indeferido
 
45
Other values (3)
 
10

Length

Max length32
Median length4
Mean length4.5134324
Min length4

Characters and Unicode

Total characters129364
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowApto
2nd rowApto
3rd rowInapto
4th rowApto
5th rowApto

Common Values

ValueCountFrequency (%)
Apto 24735
74.7%
Deferido 2571
 
7.8%
Inapto 1209
 
3.7%
Indeferido com recurso 92
 
0.3%
Indeferido 45
 
0.1%
Renúncia 7
 
< 0.1%
Deferido com recurso 2
 
< 0.1%
Pedido não conhecido com recurso 1
 
< 0.1%
(Missing) 4437
 
13.4%

Length

2022-12-06T12:48:57.152322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:57.790646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
apto 24735
85.7%
deferido 2573
 
8.9%
inapto 1209
 
4.2%
indeferido 137
 
0.5%
com 95
 
0.3%
recurso 95
 
0.3%
renúncia 7
 
< 0.1%
pedido 1
 
< 0.1%
não 1
 
< 0.1%
conhecido 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 28848
22.3%
p 25944
20.1%
t 25944
20.1%
A 24735
19.1%
e 5524
 
4.3%
r 2900
 
2.2%
d 2850
 
2.2%
i 2719
 
2.1%
f 2710
 
2.1%
D 2573
 
2.0%
Other values (13) 4617
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100510
77.7%
Uppercase Letter 28662
 
22.2%
Space Separator 192
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 28848
28.7%
p 25944
25.8%
t 25944
25.8%
e 5524
 
5.5%
r 2900
 
2.9%
d 2850
 
2.8%
i 2719
 
2.7%
f 2710
 
2.7%
n 1362
 
1.4%
a 1216
 
1.2%
Other values (7) 493
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
A 24735
86.3%
D 2573
 
9.0%
I 1346
 
4.7%
R 7
 
< 0.1%
P 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 129172
99.9%
Common 192
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 28848
22.3%
p 25944
20.1%
t 25944
20.1%
A 24735
19.1%
e 5524
 
4.3%
r 2900
 
2.2%
d 2850
 
2.2%
i 2719
 
2.1%
f 2710
 
2.1%
D 2573
 
2.0%
Other values (12) 4425
 
3.4%
Common
ValueCountFrequency (%)
192
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129356
> 99.9%
None 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 28848
22.3%
p 25944
20.1%
t 25944
20.1%
A 24735
19.1%
e 5524
 
4.3%
r 2900
 
2.2%
d 2850
 
2.2%
i 2719
 
2.1%
f 2710
 
2.1%
D 2573
 
2.0%
Other values (11) 4609
 
3.6%
None
ValueCountFrequency (%)
ú 7
87.5%
ã 1
 
12.5%

ST_PREST_CONTAS
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing3699
Missing (%)11.2%
Memory size1.9 MiB
SIM
24994 
S
2631 
NÃO
 
1621
N
 
154

Length

Max length3
Median length3
Mean length2.8105442
Min length1

Characters and Unicode

Total characters82630
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSIM
2nd rowSIM
3rd rowNÃO
4th rowSIM
5th rowSIM

Common Values

ValueCountFrequency (%)
SIM 24994
75.5%
S 2631
 
7.9%
NÃO 1621
 
4.9%
N 154
 
0.5%
(Missing) 3699
 
11.2%

Length

2022-12-06T12:48:57.906556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T12:48:58.006899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
sim 24994
85.0%
s 2631
 
8.9%
não 1621
 
5.5%
n 154
 
0.5%

Most occurring characters

ValueCountFrequency (%)
S 27625
33.4%
I 24994
30.2%
M 24994
30.2%
N 1775
 
2.1%
à 1621
 
2.0%
O 1621
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 82630
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 27625
33.4%
I 24994
30.2%
M 24994
30.2%
N 1775
 
2.1%
à 1621
 
2.0%
O 1621
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 82630
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 27625
33.4%
I 24994
30.2%
M 24994
30.2%
N 1775
 
2.1%
à 1621
 
2.0%
O 1621
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81009
98.0%
None 1621
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 27625
34.1%
I 24994
30.9%
M 24994
30.9%
N 1775
 
2.2%
O 1621
 
2.0%
None
ValueCountFrequency (%)
à 1621
100.0%

Interactions

2022-12-06T12:48:39.241367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:47:56.181439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:47:58.435770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:00.527366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:03.086968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:05.230455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:07.488258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:15.965808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:18.000319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:20.230983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:22.324603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:24.294642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:26.351538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:28.324792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:30.374547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:32.926256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:34.950718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:37.175529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:39.348051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:47:56.396808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:47:58.546245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:00.647379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:03.189998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:05.340187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:07.608071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:16.073074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:18.116525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:20.368979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:22.420449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:24.407295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:26.458941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:28.428490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:30.494781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:33.020811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:35.074492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:37.286438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:39.453879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:47:56.520308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:47:58.641846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:00.761346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:03.290870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:05.444800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:07.721186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:16.186530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:18.230724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:20.484687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:22.525438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:24.506343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:26.552751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:28.521209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:30.594610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:33.123174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:35.203917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:37.387293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:39.563167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:47:56.630299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:47:58.752693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:00.875345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:03.407362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:05.554337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:07.854862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:16.295619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:18.344245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:20.593601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:22.633559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:24.612180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:26.659495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:28.627253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:30.723128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:33.225644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:35.320745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:37.502398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-06T12:47:58.066978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-06T12:48:02.715877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:04.886616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:07.073629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:15.595722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:17.642068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:19.803737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:21.968220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:23.958396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:26.007942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:27.965872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:29.987731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:32.582637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:34.560711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:36.814520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:38.898984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:41.085667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:47:58.200109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:00.287290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:02.847216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:04.995356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:07.211159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:15.730633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:17.754080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:19.952705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:22.086348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:24.068273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:26.125654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:28.092456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:30.126885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:32.686788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:34.689798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:36.943161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:39.019296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:41.194152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:47:58.315609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:00.404113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:02.973214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:05.112950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:07.351020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:15.849208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:17.877670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:20.099093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:22.206167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:24.178166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:26.229020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:28.208502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:30.254401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:32.807750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:34.811159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:37.062694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T12:48:39.126064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-06T12:48:58.155428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-06T12:48:58.577899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-06T12:48:58.863775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-06T12:48:59.143584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-06T12:48:59.449401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-06T12:48:59.818763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-06T12:48:41.686173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-06T12:48:42.256519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-06T12:48:43.188595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ANO_ELEICAOCD_TIPO_ELEICAONM_TIPO_ELEICAONR_TURNOCD_ELEICAODS_ELEICAODT_ELEICAOTP_ABRANGENCIASG_UFSG_UENM_UECD_CARGODS_CARGOSQ_CANDIDATONR_CANDIDATONM_CANDIDATONM_URNA_CANDIDATONM_SOCIAL_CANDIDATONR_CPF_CANDIDATONM_EMAILCD_SITUACAO_CANDIDATURADS_SITUACAO_CANDIDATURACD_DETALHE_SITUACAO_CANDDS_DETALHE_SITUACAO_CANDTP_AGREMIACAONR_PARTIDOSG_PARTIDONM_PARTIDOSQ_COLIGACAONM_COLIGACAODS_COMPOSICAO_COLIGACAOCD_NACIONALIDADEDS_NACIONALIDADESG_UF_NASCIMENTOCD_MUNICIPIO_NASCIMENTONM_MUNICIPIO_NASCIMENTODT_NASCIMENTONR_IDADE_DATA_POSSENR_TITULO_ELEITORAL_CANDIDATOCD_GENERODS_GENEROCD_GRAU_INSTRUCAODS_GRAU_INSTRUCAOCD_ESTADO_CIVILDS_ESTADO_CIVILCD_COR_RACADS_COR_RACACD_OCUPACAODS_OCUPACAOVR_DESPESA_MAX_CAMPANHACD_SIT_TOT_TURNODS_SIT_TOT_TURNOST_REELEICAOST_DECLARAR_BENSNR_PROTOCOLO_CANDIDATURANR_PROCESSOCD_SITUACAO_CANDIDATO_PLEITODS_SITUACAO_CANDIDATO_PLEITOCD_SITUACAO_CANDIDATO_URNADS_SITUACAO_CANDIDATO_URNAST_CANDIDATO_INSERIDO_URNANR_FEDERACAONM_FEDERACAOSG_FEDERACAODS_COMPOSICAO_FEDERACAONM_TIPO_DESTINACAO_VOTOSCD_SITUACAO_CANDIDATO_TOTDS_SITUACAO_CANDIDATO_TOTST_PREST_CONTAS
020182ELEIÇÃO ORDINÁRIA1297Eleições Gerais Estaduais 201807/10/2018ESTADUALRJRJRIO DE JANEIRO7DEPUTADO ESTADUAL19000060135311058NORBERTO JOSE FERREIRANORBERTTO FERRERA<NA>41385250763NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO11PPPROGRESSISTAS190000050043PARTIDO ISOLADOPP1BRASILEIRA NATARJ-3NITEROI06/10/195959457422403372MASCULINO6ENSINO MÉDIO COMPLETO3CASADO(A)1BRANCA298SERVIDOR PÚBLICO MUNICIPAL10000005SUPLENTENS<NA>60079845201861900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA><NA><NA><NA><NA>
120182ELEIÇÃO ORDINÁRIA1297Eleições Gerais Estaduais 201807/10/2018ESTADUALRJRJRIO DE JANEIRO7DEPUTADO ESTADUAL19000060334914149MARISA FRANCISCA DA SILVA LIVRAMENTOMARISA LIVRAMENTO<NA>2427101745NÃO DIVULGÁVEL12APTO2DEFERIDOCOLIGAÇÃO14PTBPARTIDO TRABALHISTA BRASILEIRO190000050092TRABALHAR PARA MUDARSOLIDARIEDADE / PTB1BRASILEIRA NATARJ-3RIO DE JANEIRO01/05/197147773359803884FEMININO6ENSINO MÉDIO COMPLETO1SOLTEIRO(A)1BRANCA999OUTROS10000005SUPLENTENS<NA>60106517201861900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA><NA><NA><NA><NA>
220182ELEIÇÃO ORDINÁRIA1297Eleições Gerais Estaduais 201807/10/2018ESTADUALRJRJRIO DE JANEIRO6DEPUTADO FEDERAL1900006247304436GILSON CARVALHO VILELAVILELA<NA>2425395725NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO44PRPPARTIDO REPUBLICANO PROGRESSISTA190000050661PARTIDO ISOLADOPRP1BRASILEIRA NATARJ-3RIO DE JANEIRO08/10/197147773419203962MASCULINO6ENSINO MÉDIO COMPLETO3CASADO(A)1BRANCA233POLICIAL MILITAR25000005SUPLENTENN<NA>60360283201861900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA><NA><NA><NA><NA>
320182ELEIÇÃO ORDINÁRIA1297Eleições Gerais Estaduais 201807/10/2018ESTADUALRJRJRIO DE JANEIRO7DEPUTADO ESTADUAL19000061933320100ANA CLAUDIA COTTAS SILVACLAUDIA COTTAS<NA>3656962774NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO20PSCPARTIDO SOCIAL CRISTÃO190000050503PARTIDO ISOLADOPSC1BRASILEIRA NATARJ-3NOVA IGUAÇU11/08/197048851367203704FEMININO8SUPERIOR COMPLETO3CASADO(A)1BRANCA581DONA DE CASA10000005SUPLENTENS<NA>60304681201861900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA><NA><NA><NA><NA>
420182ELEIÇÃO ORDINÁRIA1297Eleições Gerais Estaduais 201807/10/2018ESTADUALRJRJRIO DE JANEIRO6DEPUTADO FEDERAL1900006011315545ANDREIA ALMEIDA ZITO DOS SANTOSANDREIA ZITO<NA>3607353719NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO55PSDPARTIDO SOCIAL DEMOCRÁTICO190000050208PARTIDO ISOLADOPSD1BRASILEIRA NATARJ-3DUQUE DE CAXIAS13/07/197444913030203884FEMININO8SUPERIOR COMPLETO1SOLTEIRO(A)3PARDA999OUTROS25000005SUPLENTENS<NA>60065811201861900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA><NA><NA><NA><NA>
520182ELEIÇÃO ORDINÁRIA1297Eleições Gerais Estaduais 201807/10/2018ESTADUALRJRJRIO DE JANEIRO7DEPUTADO ESTADUAL19000060870245633LIVIO TORNIAI CERQUEIRA DA SILVALIVIO TORNIAI<NA>76678580710NÃO DIVULGÁVEL12APTO2DEFERIDOCOLIGAÇÃO45PSDBPARTIDO DA SOCIAL DEMOCRACIA BRASILEIRA190000050224O RIO SEM CRISEPPS / PSDB1BRASILEIRA NATARJ-3RIO DE JANEIRO05/11/196355195913803102MASCULINO5ENSINO MÉDIO INCOMPLETO9DIVORCIADO(A)3PARDA999OUTROS10000005SUPLENTENN<NA>60166198201861900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA><NA><NA><NA><NA>
620182ELEIÇÃO ORDINÁRIA1297Eleições Gerais Estaduais 201807/10/2018ESTADUALRJRJRIO DE JANEIRO7DEPUTADO ESTADUAL19000061072665356ALTAIR LEONILDO DA SILVAALTAIR LEONILDO<NA>1617999733NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO65PC do BPARTIDO COMUNISTA DO BRASIL190000050287PARTIDO ISOLADOPC do B1BRASILEIRA NATARJ-3PINHEIRAL21/08/197048769846303022MASCULINO6ENSINO MÉDIO COMPLETO3CASADO(A)3PARDA298SERVIDOR PÚBLICO MUNICIPAL10000005SUPLENTENS<NA>60189835201861900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA><NA><NA><NA><NA>
720182ELEIÇÃO ORDINÁRIA1297Eleições Gerais Estaduais 201807/10/2018ESTADUALRJRJRIO DE JANEIRO7DEPUTADO ESTADUAL19000061474617779EDSON RICARDO SILVA DE OLIVEIRARICARDO OLIVEIRA<NA>4149466777NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO17PSLPARTIDO SOCIAL LIBERAL190000050390PARTIDO ISOLADOPSL1BRASILEIRA NATARJ-3RIO DE JANEIRO09/10/197741975367703612MASCULINO6ENSINO MÉDIO COMPLETO3CASADO(A)3PARDA233POLICIAL MILITAR10000005SUPLENTENN<NA>60235567201861900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA><NA><NA><NA><NA>
820182ELEIÇÃO ORDINÁRIA1297Eleições Gerais Estaduais 201807/10/2018ESTADUALRJRJRIO DE JANEIRO7DEPUTADO ESTADUAL19000062293840456MARLOS LUIZ DE ARAÚJO COSTAMARLOS COSTA<NA>1891690795NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO40PSBPARTIDO SOCIALISTA BRASILEIRO190000050611PARTIDO ISOLADOPSB1BRASILEIRA NATABA-3SANTA LUZ09/12/197444889427503962MASCULINO8SUPERIOR COMPLETO3CASADO(A)2PRETA131ADVOGADO10000005SUPLENTENS<NA>60344695201861900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA><NA><NA><NA><NA>
920182ELEIÇÃO ORDINÁRIA1297Eleições Gerais Estaduais 201807/10/2018ESTADUALRJRJRIO DE JANEIRO6DEPUTADO FEDERAL1900006149583644LUIZ CARLOS MARTINS FREITASTIQUINHO<NA>1083262769NÃO DIVULGÁVEL12APTO2DEFERIDOCOLIGAÇÃO36PTCPARTIDO TRABALHISTA CRISTÃO190000050393POR UM RIO MAIS HONESTOPTC / PMB1BRASILEIRA NATARJ-3RIO DE JANEIRO12/09/196949742620003962MASCULINO6ENSINO MÉDIO COMPLETO3CASADO(A)1BRANCA176COZINHEIRO25000004NÃO ELEITONN<NA>60261025201861900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA><NA><NA><NA><NA>
ANO_ELEICAOCD_TIPO_ELEICAONM_TIPO_ELEICAONR_TURNOCD_ELEICAODS_ELEICAODT_ELEICAOTP_ABRANGENCIASG_UFSG_UENM_UECD_CARGODS_CARGOSQ_CANDIDATONR_CANDIDATONM_CANDIDATONM_URNA_CANDIDATONM_SOCIAL_CANDIDATONR_CPF_CANDIDATONM_EMAILCD_SITUACAO_CANDIDATURADS_SITUACAO_CANDIDATURACD_DETALHE_SITUACAO_CANDDS_DETALHE_SITUACAO_CANDTP_AGREMIACAONR_PARTIDOSG_PARTIDONM_PARTIDOSQ_COLIGACAONM_COLIGACAODS_COMPOSICAO_COLIGACAOCD_NACIONALIDADEDS_NACIONALIDADESG_UF_NASCIMENTOCD_MUNICIPIO_NASCIMENTONM_MUNICIPIO_NASCIMENTODT_NASCIMENTONR_IDADE_DATA_POSSENR_TITULO_ELEITORAL_CANDIDATOCD_GENERODS_GENEROCD_GRAU_INSTRUCAODS_GRAU_INSTRUCAOCD_ESTADO_CIVILDS_ESTADO_CIVILCD_COR_RACADS_COR_RACACD_OCUPACAODS_OCUPACAOVR_DESPESA_MAX_CAMPANHACD_SIT_TOT_TURNODS_SIT_TOT_TURNOST_REELEICAOST_DECLARAR_BENSNR_PROTOCOLO_CANDIDATURANR_PROCESSOCD_SITUACAO_CANDIDATO_PLEITODS_SITUACAO_CANDIDATO_PLEITOCD_SITUACAO_CANDIDATO_URNADS_SITUACAO_CANDIDATO_URNAST_CANDIDATO_INSERIDO_URNANR_FEDERACAONM_FEDERACAOSG_FEDERACAODS_COMPOSICAO_FEDERACAONM_TIPO_DESTINACAO_VOTOSCD_SITUACAO_CANDIDATO_TOTDS_SITUACAO_CANDIDATO_TOTST_PREST_CONTAS
3308920222ELEIÇÃO ORDINÁRIA1546Eleições Gerais Estaduais 202202/10/2022ESTADUALRJRJRIO DE JANEIRO6DEPUTADO FEDERAL1900016386085199ALZEMIRA DE LIMA MARINSMIRIA DA AGRICULTURA<NA>61597295787NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO51PATRIOTAPATRIOTA190001682708PARTIDO ISOLADOPATRIOTA1BRASILEIRA NATARJ-3MACAÉ20/04/196062356047609064FEMININO6ENSINO MÉDIO COMPLETO9DIVORCIADO(A)1BRANCA601AGRICULTOR3176572.534NÃO ELEITONS<NA>60248093202261900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA>Válido2DeferidoS
3309020222ELEIÇÃO ORDINÁRIA1546Eleições Gerais Estaduais 202202/10/2022ESTADUALRJRJRIO DE JANEIRO7DEPUTADO ESTADUAL19000161943844410RICARDO TINOCO NOVAESKADU NOVAES<NA>90967798787NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO44UNIÃOUNIÃO BRASIL190001682176PARTIDO ISOLADOUNIÃO1BRASILEIRA NATARJ-3ITAPERUNA01/12/196656656860903882MASCULINO8SUPERIOR COMPLETO3CASADO(A)1BRANCA257EMPRESÁRIO1270629.015SUPLENTENS<NA>60201062202261900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA>Válido2DeferidoS
3309120222ELEIÇÃO ORDINÁRIA1546Eleições Gerais Estaduais 202202/10/2022ESTADUALRJRJRIO DE JANEIRO3GOVERNADOR19000160938940MARCELO RIBEIRO FREIXOMARCELO FREIXO<NA>95622780772NÃO DIVULGÁVEL12APTO2DEFERIDOCOLIGAÇÃO40PSBPARTIDO SOCIALISTA BRASILEIRO190001681434A VIDA VAI MELHORARPT/PC do B/PV / PSDB/CIDADANIA / PSOL/REDE / PSB1BRASILEIRA NATARJ-3SÃO GONÇALO12/04/196755695936403702MASCULINO8SUPERIOR COMPLETO3CASADO(A)1BRANCA277DEPUTADO17788806.164NÃO ELEITONS<NA>60129350202261900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA>Válido2DeferidoS
3309220222ELEIÇÃO ORDINÁRIA1546Eleições Gerais Estaduais 202202/10/2022ESTADUALRJRJRIO DE JANEIRO7DEPUTADO ESTADUAL19000172208327567MARCOS ANDREMARCOS ANDRE<NA>74090283787NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO27DCDEMOCRACIA CRISTÃ190001685397PARTIDO ISOLADODC1BRASILEIRA NATARJ-3RIO DE JANEIRO10/08/196260154812703102MASCULINO7SUPERIOR INCOMPLETO3CASADO(A)2PRETA298SERVIDOR PÚBLICO MUNICIPAL1270629.014NÃO ELEITONN<NA>60309316202261900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA>Válido2DeferidoS
3309320222ELEIÇÃO ORDINÁRIA1546Eleições Gerais Estaduais 202202/10/2022ESTADUALRJRJRIO DE JANEIRO6DEPUTADO FEDERAL1900016192201322WADIH NEMER DAMOUS FILHOWADIH DAMOUS<NA>54812445787NÃO DIVULGÁVEL12APTO2DEFERIDOFEDERAÇÃO13PTPARTIDO DOS TRABALHADORES190001682173FEDERAÇÃOPT/PC do B/PV1BRASILEIRA NATARJ-3RIO DE JANEIRO11/04/195666165177503022MASCULINO8SUPERIOR COMPLETO3CASADO(A)1BRANCA131ADVOGADO3176572.535SUPLENTENS<NA>60193183202261900002DEFERIDO2DEFERIDOSIM2Federação Brasil da Esperança - FE BRASILPT/PC do B/PVPT/PC do B/PVVálido2DeferidoS
3309420222ELEIÇÃO ORDINÁRIA1546Eleições Gerais Estaduais 202202/10/2022ESTADUALRJRJRIO DE JANEIRO6DEPUTADO FEDERAL1900015967722299CHARLLES BATISTA DA SILVACHARLLES BATISTA<NA>7295068783NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO22PLPARTIDO LIBERAL190001680903PARTIDO ISOLADOPL1BRASILEIRA NATARJ-3NILÓPOLIS09/06/197844999968003452MASCULINO4ENSINO FUNDAMENTAL COMPLETO3CASADO(A)3PARDA999OUTROS3176572.535SUPLENTENS<NA>60052879202261900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA>Válido2DeferidoS
3309520222ELEIÇÃO ORDINÁRIA1546Eleições Gerais Estaduais 202202/10/2022ESTADUALRJRJRIO DE JANEIRO7DEPUTADO ESTADUAL19000161366690700RODRIGO DAVID SIMPLICIOKBÇA DAVID<NA>9356072779NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO90PROSPARTIDO REPUBLICANO DA ORDEM SOCIAL190001681682PARTIDO ISOLADOPROS1BRASILEIRA NATARJ-3RIO DE JANEIRO23/11/1981411151026303962MASCULINO6ENSINO MÉDIO COMPLETO3CASADO(A)3PARDA296SERVIDOR PÚBLICO FEDERAL1270629.015SUPLENTENN<NA>60173783202261900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA>Válido2DeferidoS
3309620222ELEIÇÃO ORDINÁRIA1546Eleições Gerais Estaduais 202202/10/2022ESTADUALRJRJRIO DE JANEIRO7DEPUTADO ESTADUAL19000161221455777MUNIR FRANCISCOMUNIR NETO<NA>79168310749NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO55PSDPARTIDO SOCIAL DEMOCRÁTICO190001681605PARTIDO ISOLADOPSD1BRASILEIRA NATARJ-3VOLTA REDONDA20/10/196359398003103882MASCULINO6ENSINO MÉDIO COMPLETO3CASADO(A)1BRANCA169COMERCIANTE1270629.012ELEITO POR QPNS<NA>60161485202261900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA>Válido2DeferidoS
3309720222ELEIÇÃO ORDINÁRIA1546Eleições Gerais Estaduais 202202/10/2022ESTADUALRJRJRIO DE JANEIRO6DEPUTADO FEDERAL1900016096941989KENIA DE SOUZA GONCALVES DOS SANTOSKENIA SANTOS<NA>4475004730NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO19PODEPODEMOS190001681468PARTIDO ISOLADOPODE1BRASILEIRA NATARJ-3DUQUE DE CAXIAS15/12/1976461053886903614FEMININO8SUPERIOR COMPLETO3CASADO(A)3PARDA134ASSISTENTE SOCIAL3176572.535SUPLENTENN<NA>60137921202261900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA>Válido2DeferidoS
3309820222ELEIÇÃO ORDINÁRIA1546Eleições Gerais Estaduais 202202/10/2022ESTADUALRJRJRIO DE JANEIRO7DEPUTADO ESTADUAL19000159675222321CARLOS ROBERTO JANUÁRIOCARLOS JANUÁRIO<NA>414691784NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO22PLPARTIDO LIBERAL190001680902PARTIDO ISOLADOPL1BRASILEIRA NATARJ-3JAPERI07/03/197052760406703452MASCULINO6ENSINO MÉDIO COMPLETO9DIVORCIADO(A)3PARDA257EMPRESÁRIO1270629.015SUPLENTENS<NA>60059022202261900002DEFERIDO2DEFERIDOSIM<NA><NA><NA><NA>Válido2DeferidoS